Authors,Author(s) ID,Title,Year,Source title,Volume,Issue,Art. No.,Page start,Page end,Page count,Cited by,DOI,Link,Affiliations,Authors with affiliations,Abstract,Author Keywords,Index Keywords,Funding Details,Funding Text 1,Funding Text 2,Funding Text 3,Funding Text 4,Funding Text 5,Funding Text 6,Funding Text 7,Funding Text 8,Funding Text 9,Funding Text 10,Correspondence Address,Editors,Publisher,ISSN,ISBN,CODEN,PubMed ID,Language of Original Document,Abbreviated Source Title,Document Type,Publication Stage,Open Access,Source,EID
"Pereira A.M., Moura J.A.B., Costa E.D.B., Vieira T., Landim A.R.D.B., Bazaki E., Wanick V.","57205623421;7102993182;35618052700;8308070700;57323847900;57324408400;56523335900;","Customer models for artificial intelligence-based decision support in fashion online retail supply chains",2022,"Decision Support Systems","158",,"113795","","",,,"10.1016/j.dss.2022.113795","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129897941&doi=10.1016%2fj.dss.2022.113795&partnerID=40&md5=9ae281b24ea24d5d7672ac82728f25b8","Federal University of Campina Grande, Graduate Program on Computer Science (PPGCC), R. Aprígio Veloso, 882, PB, Campina Grande, 58428-830, Brazil; Federal University of Alagoas, Institute of Computing (IC), Av. Lourival Melo Mota, S/N, AL, Maceió, 57072-900, Brazil; University of Southampton Faculty of Humanities, Winchester School of Arts, Park Ave, Hampshire, Winchester, SO23 8DL, United Kingdom","Pereira, A.M., Federal University of Campina Grande, Graduate Program on Computer Science (PPGCC), R. Aprígio Veloso, 882, PB, Campina Grande, 58428-830, Brazil; Moura, J.A.B., Federal University of Campina Grande, Graduate Program on Computer Science (PPGCC), R. Aprígio Veloso, 882, PB, Campina Grande, 58428-830, Brazil; Costa, E.D.B., Federal University of Alagoas, Institute of Computing (IC), Av. Lourival Melo Mota, S/N, AL, Maceió, 57072-900, Brazil; Vieira, T., Federal University of Alagoas, Institute of Computing (IC), Av. Lourival Melo Mota, S/N, AL, Maceió, 57072-900, Brazil; Landim, A.R.D.B., Federal University of Campina Grande, Graduate Program on Computer Science (PPGCC), R. Aprígio Veloso, 882, PB, Campina Grande, 58428-830, Brazil; Bazaki, E., University of Southampton Faculty of Humanities, Winchester School of Arts, Park Ave, Hampshire, Winchester, SO23 8DL, United Kingdom; Wanick, V., University of Southampton Faculty of Humanities, Winchester School of Arts, Park Ave, Hampshire, Winchester, SO23 8DL, United Kingdom","Fashion is a global, multi-trillion dollar industry devoted to producing and selling clothing, footwear, and accessories to individuals or groups of people. Its sheer numbers, together with social and environmental sustainability concerns, and the move towards digitalization of customer-centric operations, make the fashion business a prime target for Decision Support Systems (DSSs). On the other hand, decision support in fashion retail is particularly problematic and embraces all major supply chain domains. Decisions in an online fashion retail supply chain (FRSC) are highly dependent on time-varying customers' preferences and product availability, often leading to a combinatorial explosion. To address such a problem, DSSs could greatly benefit from high-quality information stored in customer models (CMs), constructed by using Artificial Intelligence techniques, allowing informed decisions on how to personalize (adapt) to match the customer's needs and preferences. Combinations of CMs with recommender systems (RSs) have been increasingly utilized in fashion e-commerce to provide personalized product recommendations. Nevertheless, works on enhancing CMs for e-commerce or other decision-making chain domains are scanty. This paper offers a systematic review of the literature on fashion CMs with applications to decision-making in FRSCs, mining topics for a research agenda. Research on the theme is relevant and urgent for the fashion business, which is still in its infancy. Work on the agenda topics could benefit distinct fashion stakeholders, not just customers, and produce well-grounded decision-making in varied FRSC contexts and dynamics. © 2022 Elsevier B.V.","Artificial intelligence; Customer model; Decision support systems; Fashion; Retail supply chain; User model","Behavioral research; Decision making; Decision support systems; Electronic commerce; Online systems; Sales; Supply chains; Sustainable development; Customer preferences; Customer-modeling; Decision supports; Decisions makings; E- commerces; Fashion; Online retails; Retail supply chain; Social sustainability; User Modelling; Artificial intelligence",,"The authors thank the editor and reviewers whose comments and suggestions much improved the contents of this paper.",,,,,,,,,,"Pereira, A.M.; Federal University of Campina Grande, R. Aprígio Veloso, 882, PB, Brazil; email: artur.pereira@copin.ufcg.edu.br",,"Elsevier B.V.",01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-85129897941
"Tirkolaee E.B., Aydin N.S.","57196032874;55904216900;","Integrated design of sustainable supply chain and transportation network using a fuzzy bi-level decision support system for perishable products",2022,"Expert Systems with Applications","195",,"116628","","",,5,"10.1016/j.eswa.2022.116628","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124568002&doi=10.1016%2fj.eswa.2022.116628&partnerID=40&md5=f867e672eaaeb206f3b51462a3198884","Department of Industrial Engineering, Istinye University, Istanbul, Turkey","Tirkolaee, E.B., Department of Industrial Engineering, Istinye University, Istanbul, Turkey; Aydin, N.S., Department of Industrial Engineering, Istinye University, Istanbul, Turkey","This study introduces a fuzzy bi-level Decision Support System (DSS) to optimize a sustainable multi-level multi-product Supply Chain (SC) and co-modal transportation network for perishable products distribution. To this end, two integrated multi-objective Mixed Integer Linear Programming (MILP) models are proposed to formulate the problem. On-time delivery is taken into account as the main factor that determines model performance due to perishability of products. Optimizing the design of SC network using the first level of the proposed DSS, the transportation network configuration is provided optimally in the second level considering different modes and options. In order to contribute to the literature, mainly by addressing uncertainty and perishability, a hybrid solution technique based on possibilistic linear programming and Fuzzy Weighted Goal Programming (FWGP) approach is developed to accommodate our suggested bi-level model. This technique can deal with problem uncertainty while also ensuring the sustainability of the overall system. Lp-metric method is implemented along with three well-known quality indicators to assess the performance of the proposed solution method and quality of obtained solutions. Finally, three illustrative numerical examples are provided using the CPLEX solver to showcase the applicability of the proposed methodology and discuss the complexity of the model. Results demonstrate the efficiency of the proposed methodology in finding optimal solutions compared to Lp-metric method, such that it is able to treat a problem with more than 2.2 million variables and 1.3 million constraints in 1093.08 s. © 2022 Elsevier Ltd","Bi-level Decision Support System; Fuzzy Weighted Goal Programming; Quality indicators; Sustainable supply chain; Transportation network","Artificial intelligence; Integer programming; Linear programming; Multiobjective optimization; Product design; Supply chains; Bi-level decision support system; Fuzzy weighted goal programming; Goal-programming; Integrated designs; Perishable product; Quality indicators; Supply chain network; Sustainable supply chains; Transportation network; Uncertainty; Decision support systems",,,,,,,,,,,,"Tirkolaee, E.B.; Department of Industrial Engineering, Turkey; email: erfan.babaee@istinye.edu.tr",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-85124568002
"Goswami M., Daultani Y., Chan F.T.S., Pratap S.","24343368600;56677743400;57653390600;56667217800;","Assessing the impact of supplier benchmarking in manufacturing value chains: an Intelligent decision support system for original equipment manufacturers",2022,"International Journal of Production Research",,,,"","",,,"10.1080/00207543.2022.2075811","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131671333&doi=10.1080%2f00207543.2022.2075811&partnerID=40&md5=d4c2e8c2d891b2b2c65bedf91ab21317","Operations Management Group, Indian Institute of Management Raipur, Raipur, India; Operations Management Group, Indian Institute of Management Lucknow, Lucknow, India; Department of Decision Sciences, Macau University of Science and Technology, Taipa, Macau; Department of Mechanical Engineering, Indian Institute of Technology (BHU), Varanasi, India","Goswami, M., Operations Management Group, Indian Institute of Management Raipur, Raipur, India; Daultani, Y., Operations Management Group, Indian Institute of Management Lucknow, Lucknow, India; Chan, F.T.S., Department of Decision Sciences, Macau University of Science and Technology, Taipa, Macau; Pratap, S., Department of Mechanical Engineering, Indian Institute of Technology (BHU), Varanasi, India","This research aims to aid original equipment manufacturers (OEMs) to model, analyze, evaluate, and benchmark potential design and manufacturing suppliers based on respective product engineering teams’ efficiencies. The product engineering efficiency in this study is modeled in terms of product engineering-related attributes such as commercial lead time, number of parts, number of green features, number of end products developed, and so forth. Essentially, these parameters capture more complex interactions than simple traditional supplier selection criteria such as cost, quality, delivery, and flexibility. Due to the presence of information uncertainty in terms of bounds related to the suppliers’ related parameters, a number of data envelopment analysis (DEA) efficiency measurement models have been deployed. The proposed decision support system is novel because it models both the self-assessment type and cross-efficiency type using DEA such that maximum discrimination can be achieved amongst suppliers in the presence of interval data. The study is demonstrated for ten different sheet-metal cabin suppliers. Comparison with some well-known, relevant methods is also carried out to illustrate the validity of the proposed method. The research can specifically help supply chain managers to align the evaluation of potential suppliers with their firm's commercial considerations in the presence of information uncertainty. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","benchmarking; data envelopment analysis; decision support system; information uncertainty; original equipment manufacturers; Supplier evaluation","Artificial intelligence; Benchmarking; Data envelopment analysis; Efficiency; Industrial research; Manufacture; Product design; Sheet metal; Supply chains; Uncertainty analysis; Engineering efficiency; Engineering teams; Information uncertainty; Intelligent decision-support systems; Modeling analyzes; Original equipment manufacturers; Product engineering; Supplier Evaluations; Uncertainty; Value chains; Decision support systems",,,,,,,,,,,,"Chan, F.T.S.; Felix T.S. Chan, Macau; email: tschan@must.edu.mo",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","",Scopus,2-s2.0-85131671333
"Mohiuddin Babu M., Akter S., Rahman M., Billah M.M., Hack-Polay D.","57202131392;36058277700;57197842179;7007061155;56716330500;","The role of artificial intelligence in shaping the future of Agile fashion industry",2022,"Production Planning and Control",,,,"","",,,"10.1080/09537287.2022.2060858","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129824573&doi=10.1080%2f09537287.2022.2060858&partnerID=40&md5=f6e2fd43ced5f5a829becddf5a2139b6","School of Marketing and Management, Coventry University, Coventry, United Kingdom; School of Business, University of Wollongong, Wollongong, Australia; Lincoln International Business School, University of Lincoln, Lincoln, United Kingdom; Biotechnology and Genetic Engineering, Khulna University, Khulna, Bangladesh","Mohiuddin Babu, M., School of Marketing and Management, Coventry University, Coventry, United Kingdom; Akter, S., School of Business, University of Wollongong, Wollongong, Australia; Rahman, M., Lincoln International Business School, University of Lincoln, Lincoln, United Kingdom; Billah, M.M., Biotechnology and Genetic Engineering, Khulna University, Khulna, Bangladesh; Hack-Polay, D., Lincoln International Business School, University of Lincoln, Lincoln, United Kingdom","Artificial intelligence (AI) has become an integral part of every industry. With the emergence of big data, the industries, and more especially textile and apparel (T&A) industry, are on the brink of relationships with consumers, suppliers, and competitors. They need to handle different scenarios with a multitude of complex correlations and dependencies between them and uncertainties arising from human interaction. It has become imperative for them to manage huge amounts of data for the optimization of decision-making processes. In such circumstances, AI techniques have shown promise in every segment of the T&A value chain, from product discovery to robotic manufacturing. The potential wide-ranging applications of AI in T&A industry have found their ways into design support systems to T&A recommendation systems, intelligent tracking systems, quality control, T&A forecasting, predictive analytics in supply chain management or social networks and T&A e-commerce. The research recourses to the qualitative method in the form of systematic literature review and in-depth interviews from senior management people and industry experts. Findings identify the dimensions of AI to develop dynamic capability along with its potential impact and probable challenges. As such, the findings contribute to relevant literature and offer useful insights for academia and practitioners. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","Agile manufacturing; apparel; Artificial intelligence; big data analytics; dynamic capability; textile and fashion industry","Artificial intelligence; Big data; Data Analytics; Decision making; Predictive analytics; Supply chain management; Textile industry; Textiles; Agile manufacturing; Apparel industry; Complex correlation; Dynamics capability; Fashion industry; Humaninteraction; Integral part; Optimisations; Textile and fashion industry; Uncertainty; Behavioral research",,,,,,,,,,,,"Akter, S.; School of Business, Australia; email: sakter@uow.edu.au",,"Taylor and Francis Ltd.",09537287,,PPCOE,,"English","Prod Plann Control",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85129824573
"Cantini A., Peron M., De Carlo F., Sgarbossa F.","57219649759;57194712034;24173184800;17346547400;","A decision support system for configuring spare parts supply chains considering different manufacturing technologies",2022,"International Journal of Production Research",,,,"","",,1,"10.1080/00207543.2022.2041757","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125886013&doi=10.1080%2f00207543.2022.2041757&partnerID=40&md5=a612a5ad3988d7958bcb3879046049c2","Department of Industrial Engineering, University of Florence, Florence, Italy; Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway","Cantini, A., Department of Industrial Engineering, University of Florence, Florence, Italy, Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway; Peron, M., Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway; De Carlo, F., Department of Industrial Engineering, University of Florence, Florence, Italy; Sgarbossa, F., Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway","A well-configured spare parts supply chain (SC) can reduce costs and increase the competitiveness of spare parts retailers. A structured method for configuring spare parts SCs should be used to determine whether to centralise or decentralise inventory management, also considering hybrid configurations. Moreover, such a method should define whether or not to switch the production of spare parts from Conventional Manufacturing (CM) technologies to Additive Manufacturing (AM) ones. Indeed, AM is considered the next revolution in the field of spare parts, and the adoption of AM technologies strongly affects the characteristics of SCs. However, the choice between centralisation and decentralisation is not the subject of much scientific research, and it is also not clear when AM would be the preferable manufacturing technology for spare parts. This paper aims to assist managers and practitioners in determining how to design their spare parts SCs, thus defining both the spare parts SC configuration and the manufacturing technology to adopt through the development of a decision support system (DSS). The proposed DSS is a user-friendly decision tree, and, for the first time, it allows comparison of the total costs of SCs characterised by different degrees of centralisation with both AM and CM spare parts. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","additive manufacturing (AM); decision support system (DSS); decision tree; Spare parts logistics; supply chain configuration","3D printers; Additives; Artificial intelligence; Decision support systems; Industrial research; Inventory control; Supply chains; Additive manufacturing; Conventional manufacturing; Decision support system; Manufacturing technologies; Reduce costs; Spare part logistic; Spare parts; Spare parts supply; Structured method; Supply chain configuration; Decision trees",,,,,,,,,,,,"Cantini, A.; Department of Industrial Engineering, Viale Morgagni, 40, Italy; email: alessandra.cantini@unifi.it",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85125886013
"Sharma R., Shishodia A., Gunasekaran A., Min H., Munim Z.H.","57196545771;57201780077;56238759300;7102292247;57193992525;","The role of artificial intelligence in supply chain management: mapping the territory",2022,"International Journal of Production Research",,,,"","",,1,"10.1080/00207543.2022.2029611","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125088338&doi=10.1080%2f00207543.2022.2029611&partnerID=40&md5=ae1c78458e132d8a32520b22c2719e9f","Operations and Supply Chain Management, Jaipuria Institute of Management, Noida, India; Operations and Supply Chain Management, LM Thapar School of Management, Dera Bassi, India; School of Business Administration, Middletown, PA, United States; Department of Management, Schmidthorst College of Business, Bowling Green State University, Bowling Green, OH, United States; Faculty of Technology, Natural and Maritime Sciences, Department of Maritime Operations, University of South-Eastern Norway, Campus Vestfold (D3-52), Horten, Norway","Sharma, R., Operations and Supply Chain Management, Jaipuria Institute of Management, Noida, India; Shishodia, A., Operations and Supply Chain Management, LM Thapar School of Management, Dera Bassi, India; Gunasekaran, A., School of Business Administration, Middletown, PA, United States; Min, H., Department of Management, Schmidthorst College of Business, Bowling Green State University, Bowling Green, OH, United States; Munim, Z.H., Faculty of Technology, Natural and Maritime Sciences, Department of Maritime Operations, University of South-Eastern Norway, Campus Vestfold (D3-52), Horten, Norway","The study aims to identify the current trends, gaps, and research opportunities in research pertaining to the disruptive field of artificial intelligence (AI) applications in supply chain management (SCM). Since SCM represents managerial innovation due to its new way of integrated system thinking, SCM has emerged as one of the most fruitful business disciplines for AI applications. The study utilises bibliometric review in tracing the evolution of AI research in SCM and further synthesises decades of past AI research efforts to develop viable solutions for various supply chain problems and then proposes promising future research themes that would enrich supply chain decision-aid tools. The study identified five main research clusters through scholarly network and content analysis. The identified themes were: (a) supply chain network design (SCND), (b) supplier selection, (c) inventory planning, (d) demand planning, and (e) green supply chain management. As the role of AI in SCM continues to grow, there is a growing need for exploiting AI as a way to add value to supply chain process. The study proposes a research framework which will help academicians and practitioners in identifying current research patterns of AI in SCM. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","Artificial intelligence; bibliometric analysis; citation analysis; supply chain management; trend analysis","Artificial intelligence; Decision support systems; Systems thinking; 'current; Artificial intelligence research; Bibliometric; Bibliometrics analysis; Citation analysis; Integrated systems; Research efforts; Research opportunities; System thinkings; Trend analysis; Supply chain management",,,,,,,,,,,,"Gunasekaran, A.; School of Business Administration, E355 Olmsted Building, Penn State Harrisburg, 777 West Harrisburg Pike, United States; email: aqg6076@psu.edu",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","",Scopus,2-s2.0-85125088338
"Olan F., Arakpogun E.O., Jayawickrama U., Suklan J., Liu S.","57218370366;57218379341;55750528900;56835707500;57406537900;","Sustainable Supply Chain Finance and Supply Networks: The Role of Artificial Intelligence",2022,"IEEE Transactions on Engineering Management",,,,"","",,1,"10.1109/TEM.2021.3133104","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122570602&doi=10.1109%2fTEM.2021.3133104&partnerID=40&md5=6498ae34f3f58f9bab45ad35af0bc5d2","Newcastle Business School Ringgold Standard Institution—MOS, Northumbria University NE1 8ST, Newcastle Upon Tyne U.K. (e-mail: femi.olan@northumbria.ac.uk).; Newcastle Business School Ringgold Standard Institution—MOS, Northumbria University NE1 8ST, Newcastle Upon Tyne U.K. (e-mail: e.arakpogun@northumbria.ac.uk).; School of Business and Economics Ringgold standard Institution Loughborough, Loughborough University LE11 3TU, Loughborough U.K. (e-mail: u.jayawickrama@lboro.ac.uk).; Newcastle University Ringgold Standard Institution—NIHR Newcastle In Vitro Diagnostics, Translational and Clinical Research Institute NE2 4HH, Newcastle Upon Tyne U.K. (e-mail: jana.suklan@ncl.ac.uk).; Ringgold Standard Institution Plymouth, Plymouth Business School PL4 8AA, Plymouth U.K. (e-mail: shaofeng.liu@plymouth.ac.uk).","Olan, F., Newcastle Business School Ringgold Standard Institution—MOS, Northumbria University NE1 8ST, Newcastle Upon Tyne U.K. (e-mail: femi.olan@northumbria.ac.uk).; Arakpogun, E.O., Newcastle Business School Ringgold Standard Institution—MOS, Northumbria University NE1 8ST, Newcastle Upon Tyne U.K. (e-mail: e.arakpogun@northumbria.ac.uk).; Jayawickrama, U., School of Business and Economics Ringgold standard Institution Loughborough, Loughborough University LE11 3TU, Loughborough U.K. (e-mail: u.jayawickrama@lboro.ac.uk).; Suklan, J., Newcastle University Ringgold Standard Institution—NIHR Newcastle In Vitro Diagnostics, Translational and Clinical Research Institute NE2 4HH, Newcastle Upon Tyne U.K. (e-mail: jana.suklan@ncl.ac.uk).; Liu, S., Ringgold Standard Institution Plymouth, Plymouth Business School PL4 8AA, Plymouth U.K. (e-mail: shaofeng.liu@plymouth.ac.uk).","Supply chain finance (SCF) is receiving increasing awareness in research as a result of uncertainties in the global financing for supply chain (SC). There are limited and fragmented studies in the implementations of financial services in SC management. This article builds on recovery from the financial crisis of 2008 and posts COVID-19 pandemic, where uncertainties crippled SCF providers and brokers services. At the same time, cutting-edge technological advancements such as Artificial Intelligence (AI) are revolutionizing the processes of business ecosystem in which SCF is entrenched. This article thus adopts a fuzzy set theoretical approach to unpack the entities relationship validity for sustainable SCF mate-framework, and the originality of AI concepts to sustainable SCF to identify the issues and inefficiencies. The results indicate that AI contributes significant economic opportunities and deliver the most effective utilization of the supply networks. In addition, the article provides a theoretical contribution to financing in SC and broadens the managerial implications in improving performance. IEEE","Artificial intelligence; Artificial Intelligence (AI); Business; Companies; Finance; Neurons; SC finance (SCF); SC network; Standards; supply chain (SC); Technological innovation","Artificial intelligence; Finance; Artificial intelligence; Financial service; Supply chain; Supply chain finance; Supply chain finances; Supply chain network; Supply networks; Sustainable supply chains; Technological innovation; Uncertainty; Supply chains",,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",00189391,,IEEMA,,"English","IEEE Trans Eng Manage",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85122570602
"Eluubek kyzy I., Song H., Vajdi A., Wang Y., Zhou J.","57211683465;9734591900;56017693700;35797793900;56164546300;","Blockchain for consortium: A practical paradigm in agricultural supply chain system",2021,"Expert Systems with Applications","184",,"115425","","",,14,"10.1016/j.eswa.2021.115425","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108970198&doi=10.1016%2fj.eswa.2021.115425&partnerID=40&md5=d85465c1f418f9310f878053deb78767","School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094, China; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China","Eluubek kyzy, I., School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094, China; Song, H., School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094, China; Vajdi, A., School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China; Wang, Y., School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China; Zhou, J., School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China","Trading aspect of agricultural supply chain system is sophisticated since it consists of many stages and involves various entities/agencies. Recently, blockchain technology could prove its effectiveness to solve some of the concerns in agricultural supply chain systems. Nevertheless, maximizing profit for producers (in our study farmers) is another important concern that can be addressed by consortium establishment which blockchain technology is the best solution for this purpose due to the following reasons. First, since all the nodes in the blockchain keep the verified and synchronized version of the chain, each node can verify the transactions’ transparency. Second, blockchain technology is temper-proof that means no one can change the history of the transactions. These two main features of blockchain technology can provide a suitable ground to construct a consortium among the producers. However, there are other specific requirements that a successful consortium in agricultural supply chain system should address them that motivate us to a new design of blockchain technology. More precisely, in our design we consider the problems of trustability, scalability, and share amount assignment. For trustability, we utilize cyber physical system to ensure the quantity and quality of the products. Scalability is being addressed by adopting the concept of public service platform and proposing a new consensus algorithm. And finally share amount assignment is being solved by our improved version of ant colony optimization algorithm. Experimental results and analysis prove the effectiveness and accuracy of our proposed design for blockchain technology. © 2021 Elsevier Ltd","Agricultural supply chain system; Ant colony optimization algorithm; Blockchain; Cyber physical system","Agriculture; Ant colony optimization; Artificial intelligence; Cyber Physical System; Embedded systems; Scalability; Supply chains; Agricultural supply chain system; Agricultural supply chains; Ant Colony Optimization algorithms; Block-chain; Consensus algorithms; Cybe-physical systems; Cyber-physical systems; Public service platforms; Supply chain systems; Blockchain",,"The authors thank Dr. Binshan Lin, the Editor-in-Chief, and all the anonymous referees for numerous helpful and constructive comments on the manuscript.",,,,,,,,,,"Song, H.; School of Economics and Management, China; email: huaming@njust.edu.cn",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-85108970198
"Pournader M., Ghaderi H., Hassanzadegan A., Fahimnia B.","53878449000;56392809300;57241153800;25724319400;","Artificial intelligence applications in supply chain management",2021,"International Journal of Production Economics","241",,"108250","","",,10,"10.1016/j.ijpe.2021.108250","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114030536&doi=10.1016%2fj.ijpe.2021.108250&partnerID=40&md5=379d2cff3a716f664142950218e78280","Faculty of Business and Economics, University of Melbourne, Parkville, Australia; School of Business, Law and Entrepreneurship, Swinburne University of Technology, Hawthorn, Australia; Institute of Transport and Logistics Studies, University of Sydney Business School, Sydney, Australia","Pournader, M., Faculty of Business and Economics, University of Melbourne, Parkville, Australia; Ghaderi, H., School of Business, Law and Entrepreneurship, Swinburne University of Technology, Hawthorn, Australia; Hassanzadegan, A., Faculty of Business and Economics, University of Melbourne, Parkville, Australia; Fahimnia, B., Institute of Transport and Logistics Studies, University of Sydney Business School, Sydney, Australia","This paper presents a systematic review of studies related to artificial intelligence (AI) applications in supply chain management (SCM). Our systematic search of the related literature identifies 150 journal articles published between 1998 and 2020. A thorough bibliometric analysis is completed to develop the past and present state of this literature. A co-citation analysis on this pool of articles provides an understanding of the clusters of knowledge that constitute this research area. To further direct our discussions, we develop and validate an AI taxonomy which we use as a scale to conduct our bibliometric and co-citation analyses. The proposed taxonomy consists of three research categories of (a) sensing and interacting, (b) learning, and (c) decision making. These categories collectively establish the basis for present and future research on the application of AI methods in SCM literature and practice. Our analysis of the primary research clusters finds that learning methods are slowly getting momentum and sensing and interacting methods offer an emerging area of research. Finally, we provide a roadmap into future studies on AI applications in SCM. Our analysis underpins the importance of behavioral considerations in future studies. © 2021 Elsevier B.V.",,"Artificial intelligence; Decision making; Learning systems; Taxonomies; B-learning; Bibliometric; Bibliometrics analysis; Chain management; Co-Citation Analysis; Journal articles; Past and present; Research areas; Systematic Review; Systematic searches; Supply chain management",,,,,,,,,,,,"Fahimnia, B.; Institute of Transport and Logistics Studies, Australia; email: ben.fahimnia@sydney.edu.au",,"Elsevier B.V.",09255273,,IJPCE,,"English","Int J Prod Econ",Review,"Final","",Scopus,2-s2.0-85114030536
"Lacomme P., Rault G., Sevaux M.","56038026100;57223356322;6507301480;","Integrated decision support system for rich vehicle routing problems",2021,"Expert Systems with Applications","178",,"114998","","",,,"10.1016/j.eswa.2021.114998","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105694613&doi=10.1016%2fj.eswa.2021.114998&partnerID=40&md5=95ac69469e22a54949db2be7e97dec9f","Université de Clermont-Ferrand, LIMOS, Clermont-Ferrand, France; Université Bretagne Sud, Lab-STICC, UMR 6285, CNRS, Lorient, France; Mapotempo, Bordeaux, France","Lacomme, P., Université de Clermont-Ferrand, LIMOS, Clermont-Ferrand, France; Rault, G., Université Bretagne Sud, Lab-STICC, UMR 6285, CNRS, Lorient, France, Mapotempo, Bordeaux, France; Sevaux, M., Université Bretagne Sud, Lab-STICC, UMR 6285, CNRS, Lorient, France","Recent economic and environmental constraints push supply chain management systems to adopt closed-loop supply chain operating modes that have to address very complex problems including the end-user quality of services, environmental considerations, and daily transportation time variations. Relevant and challenging research areas require a proper coordination between the data provider software (Transport Management Software) and the operational research tool in charge of trip definition. This paper proposes a decision support system applied to the Vehicle Routing Problem able to tackle very large instances with real-life constraints. Our contribution is to propose an architecture that handle both static resolution prior to the completion of routes and update them in a dynamical context during their completions. This is implemented through a REST based API using numerous state-of-the-art operational research methods. Moreover, this system in used in practice by the Mapotempo company. © 2021 Elsevier Ltd","REST API; Rich VRP; Supply Chain; Transportation System","Artificial intelligence; Decision support systems; Information management; Supply chain management; Supply chains; Decision supports; Economic constraints; Environmental constraints; Integrated decision; Operational research; REST API; Rich VRP; Support systems; Transportation system; Vehicle Routing Problems; Vehicle routing",,,,,,,,,,,,"Rault, G.; Université Bretagne Sud, France; email: gwenael@mapotempo.com",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85105694613
"Riahi Y., Saikouk T., Gunasekaran A., Badraoui I.","57222036108;55391395500;56238759300;57208326663;","Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions",2021,"Expert Systems with Applications","173",,"114702","","",,23,"10.1016/j.eswa.2021.114702","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101126903&doi=10.1016%2fj.eswa.2021.114702&partnerID=40&md5=6114d8d9261213b25c33625b341f8083","Department of Supply Chain Management, Rabat Business School, International University of Rabat, Rabat, Morocco; Excelia Business School, CERIIM, La Rochelle, France; School of Business and Public Administration, California State University, Bakersfield, United States","Riahi, Y., Department of Supply Chain Management, Rabat Business School, International University of Rabat, Rabat, Morocco; Saikouk, T., Excelia Business School, CERIIM, La Rochelle, France; Gunasekaran, A., School of Business and Public Administration, California State University, Bakersfield, United States; Badraoui, I., Department of Supply Chain Management, Rabat Business School, International University of Rabat, Rabat, Morocco","Today's supply chains are very different from those of just a few years ago, and they continue to evolve within an extremely competitive economy. Dynamic supply chain processes require a technology that can cope with their increasing complexity. In recent years, several functional supply chain applications based on artificial intelligence (AI) have emerged, yet very few studies have addressed the applications of AI in supply chain processes. Machine learning, natural language processing, and robotics are all potential enablers of supply chain transformation. Aware of the potential advantages of AI implementation in supply chains and of the paucity of work done regarding it, we explore what researchers have done so far with respect to AI and what needs further exploration. We reviewed 136 research papers published between 1996 and 2020 from the Scopus database and provided a classification of the research material according to four critical structural dimensions (level of analytics, AI algorithms or techniques, sector or industry of application, and supply chain processes). This study is the first attempt to study the AI applications in SC from a process perspective and provides a decisional framework for adequate use of AI techniques in the different SC processes. © 2021 Elsevier Ltd","Artificial intelligence; Bibliometric analysis; Classification; Supply chain; Systematic literature review","Classification (of information); Natural language processing systems; Supply chains; Bibliometric analysis; Competitive economy; Dynamic supply chains; Future research directions; NAtural language processing; Structural dimensions; Supply chain applications; Supply chain process; Artificial intelligence",,,,,,,,,,,,"Saikouk, T.; Excelia Business School, France; email: saikoukt@excelia-group.com",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Review,"Final","",Scopus,2-s2.0-85101126903
"Pan S., Trentesaux D., McFarlane D., Montreuil B., Ballot E., Huang G.Q.","36646395300;6601942051;7006198499;7003796719;6602906195;7403425048;","Digital interoperability in logistics and supply chain management: state-of-the-art and research avenues towards Physical Internet",2021,"Computers in Industry","128",,"103435","","",,14,"10.1016/j.compind.2021.103435","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103936019&doi=10.1016%2fj.compind.2021.103435&partnerID=40&md5=c1f40cbeb75d7f674b31262c1eb2cd15","MINES ParisTech, PSL Research University, CGS -Centre de gestion scientifique, i3 UMR CNRS 9217, 60 Bd St Michel, Paris, 75006, France; LAMIH UMR CNRS 8201, Université Polytechnique Hauts-de-France, Valenciennes cedex 9, 59313, France; Institute for Manufacturing, University of Cambridge, 17 Charles Babbage Road, Cambridge, CB3 0FS, United Kingdom; Physical Internet Center, Supply Chain & Logistics Institute, H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA  30332, United States; HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong","Pan, S., MINES ParisTech, PSL Research University, CGS -Centre de gestion scientifique, i3 UMR CNRS 9217, 60 Bd St Michel, Paris, 75006, France; Trentesaux, D., LAMIH UMR CNRS 8201, Université Polytechnique Hauts-de-France, Valenciennes cedex 9, 59313, France; McFarlane, D., Institute for Manufacturing, University of Cambridge, 17 Charles Babbage Road, Cambridge, CB3 0FS, United Kingdom; Montreuil, B., Physical Internet Center, Supply Chain & Logistics Institute, H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA  30332, United States; Ballot, E., MINES ParisTech, PSL Research University, CGS -Centre de gestion scientifique, i3 UMR CNRS 9217, 60 Bd St Michel, Paris, 75006, France; Huang, G.Q., HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong","Interoperability is playing an increasing role for today's logistics and supply chain management (LSCM) because of the trends of cooperation or coopetition. Especially, digital interoperability concerning data or information exchange becomes a key enabler for the next evolutions that will massively rely upon digitalization, artificial intelligence, and autonomous systems. The notion of Physical Internet (PI) is one such evolution, an innovative worldwide logistic paradigm aimed at interconnecting and coordinating logistics networks for efficiency and sustainability. This paper investigates how digital interoperability can help interconnect logistics and supply networks as well as the operational solutions for sustainable development, and examines the new challenges and research opportunities for digital interoperability under the PI paradigm. To this end, we study the most relevant technologies for digital interoperability in LSCM, via a bibliometric analysis based on 208 papers published during 2010−2020. The results reveal that the present state-of-the-art solutions of digital interoperability are not fully aligned with PI requirements and show new challenges, research gaps and opportunities that need further discussion. Accordingly, several research avenues are suggested to advance research and applications in this area, and to achieve interconnection in logistics and supply networks for sustainability. © 2021 Elsevier B.V.","Bibliometric review; Digitalization; Interconnection; Interoperability; Logistics; Physical internet; Research avenues; State-of-the-art; Supply Chain management","Artificial intelligence; Supply chain management; Sustainable development; Autonomous systems; Bibliometric analysis; Information exchanges; Logistics and supply chain management; Logistics network; Operational solutions; Research and application; Research opportunities; Interoperability",,,,,,,,,,,,"Pan, S.; MINES ParisTech, i3 UMR CNRS 9217, 60 Bd St Michel, France; email: shenle.pan@mines-paristech.fr",,"Elsevier B.V.",01663615,,CINUD,,"English","Comput Ind",Review,"Final","",Scopus,2-s2.0-85103936019
"Hopkins J.L.","57214485891;","An investigation into emerging industry 4.0 technologies as drivers of supply chain innovation in Australia",2021,"Computers in Industry","125",,"103323","","",,34,"10.1016/j.compind.2020.103323","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097212150&doi=10.1016%2fj.compind.2020.103323&partnerID=40&md5=401b2073ffd320818ccde36a236c31c1","Department of Business Technology and Entrepreneurship, Swinburne University of Technology, Hawthorn, VIC  3122, Australia","Hopkins, J.L., Department of Business Technology and Entrepreneurship, Swinburne University of Technology, Hawthorn, VIC  3122, Australia","As supply chains recover from the impact of COVID-19, a sudden acceleration of interest in digitalization and automation is expected, as firms increasingly look towards digital technologies as sources of innovation in the wake of an extreme disruption. The purpose of this study is to utilize the experience of supply chain practitioners, to ascertain the current level of adoption of a number of key Industry 4.0 technologies, understand what preparatory measures are being taken by firms to ensure they are digitally-ready to utilise Industry 4.0 technologies, recognise how and where these technologies are likely impact supply chains, and investigate whether organisational size is a factor in technology adoption. This empirical study utilises primary data from a descriptive survey of supply chain practitioners working across a range of industry sectors and different stages in the supply chain. Whilst the findings from this research indicate that some Industry 4.0 technologies are still in the early stages of adoption, amongst Australian supply chain organisations, they clearly show which technologies are anticipated to have the greatest impact, what sectors that impact will most likely occur in, and which specific improvements they are expected to drive. Larger firms were found to be more digitally-ready than smaller firms, and a number of significant gaps were identified between expected impact and expected investment, meaning little spend is currently projected for certain technologies that are expected to have a significant impact. © 2020","3D printing; Artificial intelligence; Autonomous vehicles; Big data analytics; Blockchain; COVID-19; Industry 4.0; Internet of things; Supply chain innovation; Virtual reality","Digital storage; Industry 4.0; Current levels; Different stages; Digital technologies; Empirical studies; Industry sectors; Sources of innovation; Supply chain innovations; Technology adoption; Supply chains","Tennessee Academy of Science, TAS","The author would like to acknowledge the Supply Chain & Logistics Association of Australia (SCLAA), and in particular former VIC/TAS President Charles Edwards, for their invaluable support and assistance throughout this project.",,,,,,,,,,,,"Elsevier B.V.",01663615,,CINUD,,"English","Comput Ind",Article,"Final","",Scopus,2-s2.0-85097212150
"Gupta S., Modgil S., Meissonier R., Dwivedi Y.K.","55851943244;55314774400;23667824100;35239818900;","Artificial Intelligence and Information System Resilience to Cope With Supply Chain Disruption",2021,"IEEE Transactions on Engineering Management",,,,"","",,1,"10.1109/TEM.2021.3116770","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118575495&doi=10.1109%2fTEM.2021.3116770&partnerID=40&md5=3f47cce9eca61662293f722148cd806d","Department of Information Systems, Supply Chain Management and Decision Support, NEOMA Business School 51100, Reims France (e-mail: shivam.gupta@neoma-bs.fr).; Department of Operations Management, International Management Institute Kolkata, Kolkata 700027 India (e-mail: sach.modgil@gmail.com).; IAE Montpellier, Montpellier Research in Management 34000, Montpellier France (e-mail: regis.meissonier@umontpellier.fr).; School of Management, Swansea University SA1 8EN, Swansea U.K. and also with the Department of Management, Symbiosis Institute of Business Management, Pune and Symbiosis International (Deemed University), Pune 412115 India (e-mail: y.k.dwivedi@swansea.ac.uk).","Gupta, S., Department of Information Systems, Supply Chain Management and Decision Support, NEOMA Business School 51100, Reims France (e-mail: shivam.gupta@neoma-bs.fr).; Modgil, S., Department of Operations Management, International Management Institute Kolkata, Kolkata 700027 India (e-mail: sach.modgil@gmail.com).; Meissonier, R., IAE Montpellier, Montpellier Research in Management 34000, Montpellier France (e-mail: regis.meissonier@umontpellier.fr).; Dwivedi, Y.K., School of Management, Swansea University SA1 8EN, Swansea U.K. and also with the Department of Management, Symbiosis Institute of Business Management, Pune and Symbiosis International (Deemed University), Pune 412115 India (e-mail: y.k.dwivedi@swansea.ac.uk).","Artificial Intelligence (AI) as a technology has the potential to interpret and evaluate alternatives where multidimensional data are involved in dynamic situations such as supply chain disruption. This article aims to explore the role of resilient information systems in minimizing the risk magnitude in disruption situations in supply chain operations. The article is conducted in the qualitative mode through a semistructured interview schedule for professionals of supply chains. A thematic analysis has been used to create emerging categories. The findings of this article present critical gaps in current information systems and demonstrate how AI-oriented systems can facilitate the ecosystem of disrupted supply chains to save costs and drive efficiency on multiple parameters. The article also proposes a conceptual framework where organizational values and architectural components can be viewed jointly for quick and adequate business decisions in complex and uncertain disruptions. The framework presents the relationships among AI, information systems, and supply chain disruption. Installing appropriate AI-based data acquisition, processing, and self-training capabilities along with information system infrastructure can help organizations lessen the impact of supply chain disruption while aligning the transportation network and ensuring geographically suitable supply chains and cybersecurity. Finally, the implications for theory and practice with the limitations and scope for future research are described. IEEE","Artificial intelligence; Artificial Intelligence (AI); COVID-19; COVID-19; information system resilience; Information systems; Investment; Resilience; Stability analysis; supply chain disruption; Supply chains","Artificial intelligence; Data acquisition; Data handling; Digital storage; Information systems; Information use; Supply chains; Artificial intelligence; COVID-19; Information system resilience; Intelligence and information systems; Multidimensional data; Resilience; Stability analyze; Supply-chain disruptions; System resiliences; Investments",,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",00189391,,IEEMA,,"English","IEEE Trans Eng Manage",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85118575495
"Dora M., Kumar A., Mangla S.K., Pant A., Kamal M.M.","55101071200;57216749518;55735821600;57195573697;26027510600;","Critical success factors influencing artificial intelligence adoption in food supply chains",2021,"International Journal of Production Research",,,,"","",,8,"10.1080/00207543.2021.1959665","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112228800&doi=10.1080%2f00207543.2021.1959665&partnerID=40&md5=42d3e921fc0bd4595bda1d71fbe07aab","Reader in Operations and Supply Chain Management, Brunel Business School, Brunel University London, Uxbridge, United Kingdom; Indian Institute of Management Rohtak, Haryana, India; Jindal Global Business School, O P Jindal University, Haryana, India; Supply Chain Management, School of Strategy and Leadership, Coventry Business School, Coventry University, Coventry, United Kingdom","Dora, M., Reader in Operations and Supply Chain Management, Brunel Business School, Brunel University London, Uxbridge, United Kingdom; Kumar, A., Indian Institute of Management Rohtak, Haryana, India; Mangla, S.K., Jindal Global Business School, O P Jindal University, Haryana, India; Pant, A., Indian Institute of Management Rohtak, Haryana, India; Kamal, M.M., Supply Chain Management, School of Strategy and Leadership, Coventry Business School, Coventry University, Coventry, United Kingdom","The adoption of Artificial Intelligence (AI) in the food supply chains (FSC) can address unique challenges of food safety, quality and wastage by improving transparency and traceability. However, the technology adoption literature in FSC is still the in infancy stage, meaning little is known about the critical success factors (CSFs) that could affect the adoption of AI in FSC. Therefore, this study makes a pioneering attempt by examining the CSFs influencing the adoption of AI in the Food Supply Chain (FSC). A conceptual framework based on TOEH (Technology–Organisation–Environment–Human) theory is used to determine the CSFs influencing AI adoption in the context of Indian FSC. The rough-SWARA technique was used to rank and prioritise the CSFs for AI adoption using the relative importance weights. The results of the study indicate that technology readiness, security, privacy, customer satisfaction, perceived benefits, demand volatility, regulatory compliance, competitor pressure and information sharing among partners are the most significant CSFs for adopting AI in FSC. The findings of the study would be useful for AI technology providers, supply chain specialists and government agencies in framing appropriate policies to foster the adoption of AI in FSC the sector. © 2021 Informa UK Limited, trading as Taylor & Francis Group.","Artificial intelligence; critical success factors; Food supply chain; rough theory; sustainability; TOEH (Technology–Organisation–Environment–Human)","Customer satisfaction; Food supply; Regulatory compliance; Supply chains; Conceptual frameworks; Critical success factor; Government agencies; Importance weights; Information sharing; Perceived benefits; Technology adoption; Technology readiness; Artificial intelligence",,,,,,,,,,,,"Dora, M.; Reader in Operations and Supply Chain Management, United Kingdom; email: manoj.dora@brunel.ac.uk",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","",Scopus,2-s2.0-85112228800
"Kosasih E.E., Brintrup A.","57225996636;8837745700;","A machine learning approach for predicting hidden links in supply chain with graph neural networks",2021,"International Journal of Production Research",,,,"","",,8,"10.1080/00207543.2021.1956697","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111938707&doi=10.1080%2f00207543.2021.1956697&partnerID=40&md5=97421f3056f547bca909103f2982d9d0","Institute for Manufacturing, University of Cambridge, Cambridge, United Kingdom","Kosasih, E.E., Institute for Manufacturing, University of Cambridge, Cambridge, United Kingdom; Brintrup, A., Institute for Manufacturing, University of Cambridge, Cambridge, United Kingdom","Supply chain business interruption has been identified as a key risk factor in recent years, with high-impact disruptions due to disease outbreaks, logistic issues such as the recent Suez Canal blockage showing examples of how disruptions could propagate across complex emergent networks. Researchers have highlighted the importance of gaining visibility into procurement interdependencies between suppliers to develop more informed business contingency plans. However, extant methods such as supplier surveys rely on the willingness or ability of suppliers to share data and are not easily verifiable. In this article, we pose the supply chain visibility problem as a link prediction problem from the field of Machine Learning (ML) and propose the use of an automated method to detect potential links that are unknown to the buyer with Graph Neural Networks (GNN). Using a real automotive network as a test case, we show that our method performs better than existing algorithms. Additionally, we use Integrated Gradient to improve the explainability of our approach by highlighting input features that influence GNN’s decisions. We also discuss the advantages and limitations of using GNN for link prediction, outlining future research directions. © 2021 Informa UK Limited, trading as Taylor & Francis Group.","artificial intelligence; explainability; link prediction; machine learning; Supply chain; visibility","Data Sharing; Forecasting; Machine learning; Supply chains; Turing machines; Visibility; Automated methods; Automotive networks; Contingency plans; Emergent networks; Future research directions; Graph neural networks; Machine learning approaches; Supply chain visibility; Neural networks",,,,,,,,,,,,"Kosasih, E.E.; Institute for Manufacturing, United Kingdom; email: eek31@cam.ac.uk",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85111938707
"Bechtsis D., Tsolakis N., Iakovou E., Vlachos D.","24502912800;55921449500;6701311264;35585410100;","Data-driven secure, resilient and sustainable supply chains: gaps, opportunities, and a new generalised data sharing and data monetisation framework",2021,"International Journal of Production Research",,,,"","",,6,"10.1080/00207543.2021.1957506","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111918157&doi=10.1080%2f00207543.2021.1957506&partnerID=40&md5=fd62d60627d26545671e3e8a85f51284","Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece; Department of Industrial Engineering and Management, International Hellenic University, Thessaloniki, Greece; Department of Engineering, School of Technology, University of Cambridge, Cambridge, United Kingdom; Department of Engineering Technology and Industrial Distribution, Department of Mechanical Engineering, Mosbacher Institute for Trade, Economics and Public Policy, Texas AM University, College Stations, TX, United States","Bechtsis, D., Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece, Department of Industrial Engineering and Management, International Hellenic University, Thessaloniki, Greece; Tsolakis, N., Department of Engineering, School of Technology, University of Cambridge, Cambridge, United Kingdom; Iakovou, E., Department of Engineering Technology and Industrial Distribution, Department of Mechanical Engineering, Mosbacher Institute for Trade, Economics and Public Policy, Texas AM University, College Stations, TX, United States; Vlachos, D., Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece","The increasing exposure of global supply chains to severe disruptions such as the ones related to the COVID-19 pandemic, clearly demonstrated the need for novel data-driven risk management paradigms that monetise data from internal and external stakeholders to support supply chain security, resilience, and sustainability. We first motivate the challenges that supply chains are facing under the new realities. We then provide a critical taxonomy of the relevant literature and identify gaps which include: (i) the impact of security on supply chain operations; (ii) cost effective resiliency strategies and practices; and (iii) the social and labour dimensions of sustainability. We then propose a new generalised framework that encompasses all the identified challenges, gaps in literature and in practice, and opportunities in supply chain management research. The proposed framework is validated through a real-world case study of the organic food supply chain. This validation further highlights the need for data-driven digital technologies that enable data collection and management, secure storage and effective data processing towards data monetisation for supply chain security, cost-competitive resilience, and sustainability across end-to-end operations. © 2021 Informa UK Limited, trading as Taylor & Francis Group.","artificial intelligence; blockchain; data sharing and monetisation; security; supply chain resilience; sustainability","Cost effectiveness; Costs; Digital storage; Food supply; Information management; Risk management; Supply chain management; Sustainable development; Cost competitive; Data collection; Digital technologies; External stakeholders; Global supply chain; Supply chain operation; Supply chain security; Sustainable supply chains; Data Sharing",,,,,,,,,,,,"Bechtsis, D.; Alexander Campus of IHUGreece; email: dimbec@ihu.gr",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","",Scopus,2-s2.0-85111918157
"Cadden T., Dennehy D., Mantymaki M., Treacy R.","8223957300;55787347300;24725250200;57196478804;","Understanding the influential and mediating role of cultural enablers of AI integration to supply chain",2021,"International Journal of Production Research",,,,"","",,2,"10.1080/00207543.2021.1946614","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110592444&doi=10.1080%2f00207543.2021.1946614&partnerID=40&md5=bd3a7d45c30355a3f99c7473815b87a1","Department of Management, Leadership and Marketing, Ulster University, Derry, United Kingdom; Digital Transformation Research Centre, Ajman University, Ajman, United Arab Emirates; Business Information Systems Department, National University of Ireland Galway, Galway, Ireland; Information Systems Science Department, University of Turku, Turku, Finland; Department of Business and Administration, University of Gothenburg, Gothenburg, Sweden","Cadden, T., Department of Management, Leadership and Marketing, Ulster University, Derry, United Kingdom, Digital Transformation Research Centre, Ajman University, Ajman, United Arab Emirates; Dennehy, D., Business Information Systems Department, National University of Ireland Galway, Galway, Ireland; Mantymaki, M., Information Systems Science Department, University of Turku, Turku, Finland; Treacy, R., Department of Business and Administration, University of Gothenburg, Gothenburg, Sweden","Artificial Intelligence (AI) has been claimed to offer transformational power across industries and sectors. To date, research has largely focused on the technical characteristics of AI and its influence on organisational capabilities. Despite the hype surrounding AI, there is a scarcity of rigorous research that examines the organisational and behavioural factors that foster AI integration in supply chains is lacking. This quantitative study addresses this gap in knowledge by developing a research hypothesis that examines the relationships between supply chain culture and AI. We extend the generalisability of culture to provide novel insights about AI-driven supply chains that have not been reported in previous studies. The findings demonstrate the influential role that cultural enablers have on the successful integration of AI technologies in supply chains, which has implications for operations and supply chain management. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","AI; Artificial intelligence; culture; operations management; supply chains","Supply chain management; AI Technologies; Mediating roles; Operations and supply chain managements; Organisational; Organisational capabilities; Quantitative study; Rigorous research; Artificial intelligence",,,,,,,,,,,,"Dennehy, D.; Business Information Systems Department, Ireland; email: denis.dennehy@nuigalway.ie",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85110592444
"Belhadi A., Kamble S., Fosso Wamba S., Queiroz M.M.","57192378881;36864045000;14833520200;56370773200;","Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework",2021,"International Journal of Production Research",,,,"","",,14,"10.1080/00207543.2021.1950935","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110589528&doi=10.1080%2f00207543.2021.1950935&partnerID=40&md5=cb3d3d745f86acca9220d5c27b2000b6","Cadi Ayyad University, Marrakech, Morocco; EDHEC Business School, Roubaix, France; Toulouse Business School, Toulouse, Midi-Pyrenees, France; Paulista University–UNIP, São Paulo, Brazil","Belhadi, A., Cadi Ayyad University, Marrakech, Morocco; Kamble, S., EDHEC Business School, Roubaix, France; Fosso Wamba, S., Toulouse Business School, Toulouse, Midi-Pyrenees, France; Queiroz, M.M., Paulista University–UNIP, São Paulo, Brazil","Artificial Intelligence (AI) offers a promising solution for building and promoting more resilient supply chains. However, the literature is highly dispersed regarding the application of AI in supply-chain management. The literature to date lacks a decision-making framework for identifying and applying powerful AI techniques to build supply-chain resilience (SCRes), curbing advances in research and practice on this interesting interface. In this paper, we propose an integrated Multi-criteria decision-making (MCDM) technique powered by AI-based algorithms such as Fuzzy systems, Wavelet Neural Networks (WNN) and Evaluation based on Distance from Average Solution (EDAS) to identify patterns in AI techniques for developing different SCRes strategies. The analysis was informed by data collected from 479 manufacturing companies to determine the most significant AI applications used for SCRes. The findings show that fuzzy logic programming, machine learning big data, and agent-based systems are the most promising techniques used to promote SCRes strategies. The study findings support decision-makers by providing an integrated decision-making framework to guide practitioners in AI deployment for building SCRes. © 2021 Informa UK Limited, trading as Taylor & Francis Group.","artificial intelligence; EDAS; fuzzy system; multi-criteria decision-making; Supply-chain resilience; wavelet neural networks","Decision making; Fuzzy logic; Logic programming; Supply chain management; Agent-based systems; AI applications; Decision-making frameworks; Integrated decision makings; Manufacturing companies; Multi-criteria decision making; Supply chain resiliences; Wavelet neural networks; Artificial intelligence",,,,,,,,,,,,"Kamble, S.; EDHEC Business SchoolFrance; email: sachin.kamble@edhec.edu",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","",Scopus,2-s2.0-85110589528
"Olan F., Liu S., Suklan J., Jayawickrama U., Arakpogun E.","57218370366;24174685600;56835707500;55750528900;57218379341;","The role of Artificial Intelligence networks in sustainable supply chain finance for food and drink industry",2021,"International Journal of Production Research",,,,"","",,12,"10.1080/00207543.2021.1915510","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104848735&doi=10.1080%2f00207543.2021.1915510&partnerID=40&md5=b054feada067d00c88bfe6e711a9afa6","Newcastle Business School, Northumbria University, Newcastle Upon Tyne, United Kingdom; Plymouth Business School, University of Plymouth, Plymouth, United Kingdom; NIHR Newcastle IVD Co-operative Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom; School of Business and Economics, Loughborough University, Loughborough, United Kingdom","Olan, F., Newcastle Business School, Northumbria University, Newcastle Upon Tyne, United Kingdom; Liu, S., Plymouth Business School, University of Plymouth, Plymouth, United Kingdom; Suklan, J., NIHR Newcastle IVD Co-operative Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom; Jayawickrama, U., School of Business and Economics, Loughborough University, Loughborough, United Kingdom; Arakpogun, E., Newcastle Business School, Northumbria University, Newcastle Upon Tyne, United Kingdom","In the last decade, food and drink supply chain management has become an important part of global operations strategy. The global food and drink industries (FDIs) is establishing supply chain operations across countries as a result of increasing demand, this expansion has created challenges in coordinating operations that connect multi-suppliers, one as such is the financial enabler for the multi-layered supply chain network. However, literature on artificial intelligence (AI) in FDIs is limited, this study explores AI theory in supply chain networks and alternative supply chain financing for the FDIs. This study proposes a new conceptual framework based on theoretical contributions identified through literature, a conceptual framework is established and further developed to a meta-framework. This study explored the set-theoretic comparative approach for data analysis, the outcomes of this research suggest that the probable contributions of supply chain networks driven by AI technologies provide a sustainable financing stream for the food and drink supply chain. © 2021 Informa UK Limited, trading as Taylor & Francis Group.","Artificial intelligence; food and drink industries; supply chain finance; supply networks; sustainability","Data streams; Finance; Network layers; Supply chain management; Comparative approach; Conceptual frameworks; Drink industries; Global operations; Supply chain financings; Supply chain network; Supply chain operation; Sustainable supply chains; Artificial intelligence",,,,,,,,,,,,"Olan, F.; Newcastle Business School, United Kingdom; email: femiolan@outlook.com",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85104848735
"Fosso Wamba S., Queiroz M.M., Guthrie C., Braganza A.","14833520200;56370773200;56160059600;6701735121;","Industry experiences of artificial intelligence (AI): benefits and challenges in operations and supply chain management",2021,"Production Planning and Control",,,,"","",,2,"10.1080/09537287.2021.1882695","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104719950&doi=10.1080%2f09537287.2021.1882695&partnerID=40&md5=8c8ed03d82e20539fd9ab184101fa536","Information, Operations and Management Sciences, TBS Business School, Toulouse, France; Postgraduate Program in Business Administration, Paulista University–UNIP, São Paulo, Brazil; College of Business, Arts and Social Sciences, Brunel Business School, Uxbridge, United Kingdom","Fosso Wamba, S., Information, Operations and Management Sciences, TBS Business School, Toulouse, France; Queiroz, M.M., Postgraduate Program in Business Administration, Paulista University–UNIP, São Paulo, Brazil; Guthrie, C., Information, Operations and Management Sciences, TBS Business School, Toulouse, France; Braganza, A., College of Business, Arts and Social Sciences, Brunel Business School, Uxbridge, United Kingdom","This editorial aims to present the papers accepted for the special issue (SI) 'Industry experiences of Artificial Intelligence (AI): benefits and challenges in operations and supply chain management.' First, we provide a brief introduction considering the relationship between AI and operations and supply chain management (OSCM) by highlighting some companies already using and practical insights. In sequence, we introduce the papers selected for the SI. The last section gives some intriguing and challenging research directions for scholars and industry practitioners by highlighting potential topics, research opportunities, and possible benefits. Through this SI, we look forward to helping industry practitioners, policy-makers, scholars, and all interested in this field to gain more knowledge about AI applications and insights in relation to OSCM. © 2021 Informa UK Limited, trading as Taylor & Francis Group.","AI; artificial intelligence; Editorial; industry experiences; operations and supply chain management; production systems","Supply chain management; AI applications; Industry experience; Look-forward; Operations and supply chain managements; Policy makers; Potential topics; Research opportunities; Artificial intelligence",,,,,,,,,,,,"Fosso Wamba, S.; Information, 1 Place Alphonse Jourdain, France",,"Taylor and Francis Ltd.",09537287,,PPCOE,,"English","Prod Plann Control",Article,"Article in Press","",Scopus,2-s2.0-85104719950
"Helo P., Hao Y.","6506880314;56026541100;","Artificial intelligence in operations management and supply chain management: an exploratory case study",2021,"Production Planning and Control",,,,"","",,14,"10.1080/09537287.2021.1882690","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103624700&doi=10.1080%2f09537287.2021.1882690&partnerID=40&md5=c1770317c46d77ec9e9d9f7caa333d33","Department of Industrial Management, University of Vaasa, Vaasa, Finland","Helo, P., Department of Industrial Management, University of Vaasa, Vaasa, Finland; Hao, Y., Department of Industrial Management, University of Vaasa, Vaasa, Finland","With the development and evolution of information technology, competition has become more and more intensive on a global scale. Many companies have forecast that the future of operation and supply chain management (SCM) may change dramatically, from planning, scheduling, optimisation, to transportation, with the presence of artificial intelligence (AI). People will be more and more interested in machine learning, AI, and other intelligent technologies, in terms of SCM. Within this context, this particular research study provides an overview of the concept of AI and SCM. It then focuses on timely and critical analysis of AI-driven supply chain research and applications. In this exploratory research, the emerging AI-based business models of different case companies are analysed. Their relevant AI solutions and related values to companies are also evaluated. As a result, this research identifies several areas of value creation for the application of AI in the supply chain. It also proposes an approach to designing business models for AI supply chain applications. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","Artificial intelligence; operations management; supply chain management","Scheduling; Supply chain management; Critical analysis; Exploratory case studies; Exploratory research; Intelligent technology; Operations management; Research and application; Supply chain applications; Supply chain managements (SCM); Artificial intelligence",,,,,,,,,,,,"Hao, Y.; Department of Industrial Management, Finland",,"Taylor and Francis Ltd.",09537287,,PPCOE,,"English","Prod Plann Control",Article,"Article in Press","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85103624700
"Dubey R., Bryde D.J., Foropon C., Tiwari M., Dwivedi Y., Schiffling S.","36991875200;9335931400;55570897100;57221261105;35239818900;56495549700;","An investigation of information alignment and collaboration as complements to supply chain agility in humanitarian supply chain",2021,"International Journal of Production Research","59","5",,"1586","1605",,47,"10.1080/00207543.2020.1865583","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098624359&doi=10.1080%2f00207543.2020.1865583&partnerID=40&md5=1e138be274a22c35fbbd5c1b17aed0e0","Liverpool Business School, Liverpool John Moore’s University, Liverpool, United Kingdom; Montpellier Business School, Montpellier Research in Management, Montpellier, France; School of Management, Swansea University, Swansea, United Kingdom","Dubey, R., Liverpool Business School, Liverpool John Moore’s University, Liverpool, United Kingdom; Bryde, D.J., Liverpool Business School, Liverpool John Moore’s University, Liverpool, United Kingdom; Foropon, C., Montpellier Business School, Montpellier Research in Management, Montpellier, France; Tiwari, M., Liverpool Business School, Liverpool John Moore’s University, Liverpool, United Kingdom; Dwivedi, Y., School of Management, Swansea University, Swansea, United Kingdom; Schiffling, S., Liverpool Business School, Liverpool John Moore’s University, Liverpool, United Kingdom","Our study examines the relationship between information alignment (IA), collaboration (CO) and supply chain agility (SCAG) under the moderating effects of artificial intelligence-driven big data analytics capability (AI-BDAC) and intergroup leadership (IGL). We have grounded our theoretical model in the resource-based view (RBV) and contingency theory and further tested our research hypotheses using multi-informant data collected using a web-based pre-tested instrument from 613 individuals working in 193 humanitarian organisations drawn from 24 countries located on various continents across the globe. We tested our research hypotheses using variance-based structural equation modelling (PLS-SEM). Our study offers interesting results which help to advance the theoretical debates surrounding technology-driven supply chain agility in the context of humanitarian settings. We further provide some directions to managers engaged in disaster relief operations, who are contemplating using emerging technologies to enhance collaboration and supply chain agility. Finally, we have outlined the limitations of our study and offer some future research directions. © 2020 Informa UK Limited, trading as Taylor & Francis Group.","artificial intelligence; big data analytics; humanitarian supply chain; Information alignment; intergroup leadership; supply chain agility","Advanced Analytics; Alignment; Artificial intelligence; Data Analytics; Disaster prevention; Disaster relief operations; Emerging technologies; Future research directions; Information alignment; Resource-based view; Structural equation modelling; Supply chain agility; Theoretical modeling; Supply chains",,,,,,,,,,,,"Dubey, R.; Rameshwar Dubey, United Kingdom; email: r.dubey@ljmu.ac.uk",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85098624359
"Hewitt M., Frejinger E.","34872009800;15123673200;","Data-driven optimization model customization",2020,"European Journal of Operational Research","287","2",,"438","451",,3,"10.1016/j.ejor.2020.05.010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086372620&doi=10.1016%2fj.ejor.2020.05.010&partnerID=40&md5=241bd179aaea5fdc2c8e7d9b92a48909","Quinlan School of Business, Loyola University Chicago, United States; CIRRELT and Department of Computer Science and Operations Research, Université de Montréal, Canada","Hewitt, M., Quinlan School of Business, Loyola University Chicago, United States; Frejinger, E., CIRRELT and Department of Computer Science and Operations Research, Université de Montréal, Canada","When embedded in software-based decision support systems, optimization models can greatly improve organizational planning. In many industries, there are classical models that capture the fundamentals of general planning decisions (e.g., designing a delivery route). However, these models are generic and often require customization to truly reflect the realities of specific operational settings. Yet, such customization can be an expensive and time-consuming process. At the same time, popular cloud computing software platforms such as Software as a Service (SaaS) are not amenable to customized software applications. We present a framework that has the potential to autonomously customize optimization models by learning mathematical representations of customer-specific business rules from historical data derived from model solutions and implemented plans. Because of the wide-spread use in practice of mixed integer linear programs (MILP) and the power of MILP solvers, the framework is designed for MILP models. It uses a common mathematical representation for different optimization models and business rules, which it encodes in a standard data structure. As a result, a software provider employing this framework can develop and maintain a single code-base while meeting the needs of different customers. We assess the effectiveness of this framework on multiple classical MILPs used in the planning of logistics and supply chain operations and with different business rules that must be observed by implementable plans. Computational experiments based on synthetic data indicate that solutions to the customized optimization models produced by the framework are regularly of high-quality. © 2020 Elsevier B.V.","Decision support systems; Mixed integer linear programming; Optimization modeling; Statistical learning","Application programs; Artificial intelligence; Decision support systems; Embedded systems; Integer programming; Supply chains; Cloud computing softwares; Computational experiment; Data-driven optimization; Mathematical representations; Mixed integer linear program; Optimization models; Organizational planning; Supply chain operation; Software as a service (SaaS)",,"We acknowledge the funding through the Canada Research Chair in Demand Forecasting and Optimization of Transport Systems that the second author holds.",,,,,,,,,,"Hewitt, M.; Quinlan School of Business, United States; email: mhewitt3@luc.edu",,"Elsevier B.V.",03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","",Scopus,2-s2.0-85086372620
"Greif T., Stein N., Flath C.M.","57217853651;57191160950;54392239000;","Peeking into the void: Digital twins for construction site logistics",2020,"Computers in Industry","121",,"103264","","",,33,"10.1016/j.compind.2020.103264","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087681639&doi=10.1016%2fj.compind.2020.103264&partnerID=40&md5=5e9d65eb855f10bad9cd079f9f8ce37e","Lehrstuhl für Wirtschaftsinformatik und Informationsmanagement, Julius-Maximilians-University, Josef-Stangl-Platz 2, Würzburg, 97070, Germany","Greif, T., Lehrstuhl für Wirtschaftsinformatik und Informationsmanagement, Julius-Maximilians-University, Josef-Stangl-Platz 2, Würzburg, 97070, Germany; Stein, N., Lehrstuhl für Wirtschaftsinformatik und Informationsmanagement, Julius-Maximilians-University, Josef-Stangl-Platz 2, Würzburg, 97070, Germany; Flath, C.M., Lehrstuhl für Wirtschaftsinformatik und Informationsmanagement, Julius-Maximilians-University, Josef-Stangl-Platz 2, Würzburg, 97070, Germany","Construction is one of the least-digitized industries in the economy. To rein in the rising costs of building activities, digital transformation is one of the pillars that industry leaders rely on. A case in point are logistics processes which are characterized by very limited visibility and inefficient organization. To progress beyond this current state of the art, we conceptualize the idea of a lightweight digital twin for non-high-tech industries. In collaboration with a leading supplier of building materials, we explore the opportunities offered by digital silo twin capabilities. Focusing on fill level monitoring we identify diverse opportunities for generating informational, automational and transformational business value. Leveraging new information sources for the redesign of core business processes drastically increases the complexity of operational decision-making. To tap into these opportunities, we design and implement a decision support system for silo dispatch and replenishment activity. © 2020 Elsevier B.V.","Construction industry; Decision support system; Digital twin; Smart logistics; Supply chain management","Artificial intelligence; Decision making; Digital twin; Building activities; Construction sites; Design and implements; Digital transformation; High tech industry; Information sources; Limited visibility; Operational decision making; Decision support systems",,,,,,,,,,,,"Flath, C.M.; Lehrstuhl für Wirtschaftsinformatik und Informationsmanagement, Germany; email: christoph.flath@uni-wuerzburg.de",,"Elsevier B.V.",01663615,,CINUD,,"English","Comput Ind",Article,"Final","",Scopus,2-s2.0-85087681639
"Lechner G., Reimann M.","57069750900;8917248000;","Integrated decision-making in reverse logistics: an optimisation of interacting acquisition, grading and disposition processes",2020,"International Journal of Production Research","58","19",,"5786","5805",,17,"10.1080/00207543.2019.1659518","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071384114&doi=10.1080%2f00207543.2019.1659518&partnerID=40&md5=100a8751beb87651665b2ae8ea825e8e","Department of Production and Operations Management, University of Graz, Universitaetsstrasse 15/E3, Graz, 8010, Austria","Lechner, G., Department of Production and Operations Management, University of Graz, Universitaetsstrasse 15/E3, Graz, 8010, Austria; Reimann, M., Department of Production and Operations Management, University of Graz, Universitaetsstrasse 15/E3, Graz, 8010, Austria","In view of global environmental and social challenges the transition towards a Circular Economy is considered as a crucial factor for sustainable development. Therefore, the replacement of traditional linear business models involving product discard at the end of product life with concepts focusing on re-use of resources is essential. Reverse Logistics and Closed-loop Supply Chains are seen to be key elements of such a transition. Motivated by findings from a case study of an independent reprocessing company, we address integrated decision-making in Reverse Logistics in this paper. We present a non-linear optimisation model with interrelated processes in terms of acquisition of used products, grading for determination of product quality and reprocessing disposition. The decisions to be made concern the effort spent for active acquisition of used products and the number of reprocessed goods; both decisions are influenced by heterogeneous condition of used products. The consideration of deterministic and stochastic demand facilitates the representation of a variety of business cases. For both demand types we provide analytical insights in the form of complete strategies consisting of different scenarios which allow optimal decision-making under variable conditions. Numerical examples complement insights into the model by conducting a sensitivity analysis of relevant model parameters. © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","decision support systems; integrated decision-making; newsvendor; non-linear programming; reverse logistics","Artificial intelligence; Decision support systems; Grading; Mergers and acquisitions; Nonlinear programming; Numerical methods; Sensitivity analysis; Stochastic systems; Supply chains; Sustainable development; Closed-loop supply chain; Heterogeneous conditions; Integrated decision makings; Newsvendors; Non-linear optimisation; Optimal decision making; Reverse logistics; Variable conditions; Decision making","14974; Karl-Franzens-Universität Graz","This work has been supported by funds of the Oesterreichische National Bank (Anniversary Fund, project number: 14974). The authors gratefully acknowledge this financial support. The authors also acknowledge the financial support by the University of Graz.",,,,,,,,,,"Lechner, G.; Department of Production and Operations Management, Universitaetsstrasse 15/E3, Austria; email: gernot.lechner@uni-graz.at",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85071384114
"Badakhshan E., Humphreys P., Maguire L., McIvor R.","57222104216;7005959792;7006038431;7004499918;","Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain",2020,"International Journal of Production Research","58","17",,"5253","5279",,5,"10.1080/00207543.2020.1715505","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078450211&doi=10.1080%2f00207543.2020.1715505&partnerID=40&md5=ea333090971fb5a2c504d7580b8dca64","Ulster Business School, Ulster University, Derry, United Kingdom; Faculty of Computing, Engineering, and Built Environment, Ulster University, Jordanstown, United Kingdom","Badakhshan, E., Ulster Business School, Ulster University, Derry, United Kingdom; Humphreys, P., Ulster Business School, Ulster University, Derry, United Kingdom; Maguire, L., Faculty of Computing, Engineering, and Built Environment, Ulster University, Jordanstown, United Kingdom; McIvor, R., Ulster Business School, Ulster University, Derry, United Kingdom","The bullwhip effect (BWE) is a phenomenon, which is caused by ineffective inventory decisions made by supply chain members. In addition to known inefficiencies caused by the bullwhip effect within a supply chain product flow, such as excessive inventory, it can also lead to inefficiencies in cash flow such as the cash flow bullwhip (CFB). The CFB reduces the efficiency of the supply chain (SC) through heterogeneous distribution of cash among supply chain members. This paper aims to decrease both the BWE and the CFB across a SC through applying a simulation-based optimisation approach, which integrates system dynamics (SD) simulation and genetic algorithms. For this purpose, cash flow modelling is incorporated into the SD structure of the beer distribution game (BG) to develop the CFB function. A multi objective optimisation model is then integrated with the SD-BG simulation model. Finally, a genetic algorithm (GA) is applied to determine the optimal values for the inventory, supply line, and financial decision parameters. Results show that the proposed integrated framework leads to efficient liquidity management in the SC in addition to cost management. © 2020 Ulster University.","artificial intelligence; genetic algorithms and the bullwhip effect; simulation; supply chain management","Artificial intelligence; Multiobjective optimization; Supply chain management; System theory; Beer distribution games; Bullwhip effects; Financial decisions; Heterogeneous distributions; Integrated frameworks; Inventory decisions; simulation; Simulation based optimisation; Genetic algorithms",,,,,,,,,,,,"McIvor, R.; Ulster Business School, United Kingdom; email: r.mcivor@ulster.ac.uk",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85078450211
"Brevik E., Lauen A.Ø., Rolke M.C.B., Fagerholt K., Hansen J.R.","57214222446;57214229856;57214230411;6602817901;57190966889;","Optimisation of the broiler production supply chain",2020,"International Journal of Production Research","58","17",,"5218","5237",,4,"10.1080/00207543.2020.1713415","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078400335&doi=10.1080%2f00207543.2020.1713415&partnerID=40&md5=0e6cea440636cdd38c458140e2ebf208","Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway; Norsk Kylling AS, Støren, Norway","Brevik, E., Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway; Lauen, A.Ø., Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway; Rolke, M.C.B., Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway; Fagerholt, K., Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway; Hansen, J.R., Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway, Norsk Kylling AS, Støren, Norway","In this paper, we propose a mixed integer programming (MIP) model for the Chicken Flock Sizing, Allocation and Scheduling Problem (CFSASP), which is an important planning problem in the broiler production supply chain. To solve the CFSASP efficiently, two variants of rolling horizon heuristics (RHHs) have been developed and applied on the case of a Norwegian broiler production company. Computational results show that the RHHs successfully obtain high-quality solutions within a reasonable time. The value of optimisation is verified through comparison with the case company's plans, where the solutions from optimisation outperforms the current solutions. Sensitivity analyses are also conducted to provide managerial insights regarding certain strategic decisions, such as how many and which days to use for hatching of chickens. Due to the promising results, the case company is now implementing an optimisation-based decision support system based on the MIP model and solution methods shown in this paper. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","broiler production; case study; mixed integer programming; rolling horizon heuristic; supply chain optimisation","Animals; Artificial intelligence; Decision support systems; Sensitivity analysis; Supply chains; Allocation and scheduling; Broiler productions; Computational results; High-quality solutions; Mixed integer programming; Mixed integer programming model; Rolling horizon; Supply chain optimisation; Integer programming",,,,,,,,,,,,"Fagerholt, K.; Department of Industrial Economics and Technology Management, Norway; email: kjetil.fagerholt@ntnu.no",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85078400335
"Rodríguez-Espíndola O., Chowdhury S., Beltagui A., Albores P.","56444362800;57201962502;24922784100;6508306073;","The potential of emergent disruptive technologies for humanitarian supply chains: the integration of blockchain, Artificial Intelligence and 3D printing",2020,"International Journal of Production Research","58","15",,"4610","4630",,63,"10.1080/00207543.2020.1761565","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084819819&doi=10.1080%2f00207543.2020.1761565&partnerID=40&md5=831c019a7437dd8cec860d0da7a5b0a7","Aston Business School, Aston University, Birmingham, United Kingdom","Rodríguez-Espíndola, O., Aston Business School, Aston University, Birmingham, United Kingdom; Chowdhury, S., Aston Business School, Aston University, Birmingham, United Kingdom; Beltagui, A., Aston Business School, Aston University, Birmingham, United Kingdom; Albores, P., Aston Business School, Aston University, Birmingham, United Kingdom","The growing importance of humanitarian operations has created an imperative to overcome the complications currently recorded in the field. Challenges such as delays, congestion, poor communication and lack of accountability may represent opportunities to test the reported advantages of emergent disruptive technologies. Meanwhile, the literature on humanitarian supply chains looks at isolated applications of technology and lacks a framework for understanding challenges and solutions, a gap that this article aims to fill. Using a case study based on the flood of Tabasco of 2007 in Mexico, this research identifies solutions based on the use of emergent disruptive technologies. Furthermore, this article argues that the integration of different technologies is essential to deliver real benefits to the humanitarian supply chain. As a result, it proposes a framework to improve the flow of information, products and financial resources in humanitarian supply chains integrating three emergent disruptive technologies; Artificial Intelligence, Blockchain and 3D Printing. The analysis presented shows the potential of the framework to reduce congestion in the supply chain, enhance simultaneous collaboration of different stakeholders, decrease lead times, increase transparency, traceability and accountability of material and financial resources, and allow victims to get involved in the fulfilment of their own needs. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.","3D printing; Artificial Intelligence; blockchain; disaster management; disruptive technologies; Humanitarian logistics","Artificial intelligence; Blockchain; Supply chains; 3-D printing; Disruptive technology; Financial resources; Humanitarian operations; Lead time; Me-xico; Simultaneous collaborations; 3D printers",,,,,,,,,,,,"Rodríguez-Espíndola, O.; Aston Business School, United Kingdom; email: o.rodriguez-espindola@aston.ac.uk",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85084819819
"Dubey R., Gunasekaran A., Childe S.J., Bryde D.J., Giannakis M., Foropon C., Roubaud D., Hazen B.T.","36991875200;56238759300;6701664870;9335931400;22634489100;55570897100;56046754600;53663608700;","Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations",2020,"International Journal of Production Economics","226",,"107599","","",,112,"10.1016/j.ijpe.2019.107599","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077155060&doi=10.1016%2fj.ijpe.2019.107599&partnerID=40&md5=5f3564e0c97fe3447db9606b3356e270","Montpellier Business School, Montpellier Research in Management, 2300 Avenue des Moulins, Montpellier34185, France; School of Business and Public Administration, California State University, Bakersfield, 9001 Stockdale Highway, Bakersfield, CA  93311-1022, United States; Plymouth Business School, Plymouth University, Plymouth, PL4 8AA, United Kingdom; Liverpool Business School, Liverpool John Moore's University, Liverpool, Merseyside L3 5UG, United Kingdom; Audencia Business School 8 route de la Jonelière-B.P. 31222, Nantes Cedex 344312, France; Logistikum, University of Applied Sciences, Upper Austria Wehrgrabengasse 1-3, Steyr, 4400, Austria","Dubey, R., Montpellier Business School, Montpellier Research in Management, 2300 Avenue des Moulins, Montpellier34185, France; Gunasekaran, A., School of Business and Public Administration, California State University, Bakersfield, 9001 Stockdale Highway, Bakersfield, CA  93311-1022, United States; Childe, S.J., Plymouth Business School, Plymouth University, Plymouth, PL4 8AA, United Kingdom; Bryde, D.J., Liverpool Business School, Liverpool John Moore's University, Liverpool, Merseyside L3 5UG, United Kingdom; Giannakis, M., Audencia Business School 8 route de la Jonelière-B.P. 31222, Nantes Cedex 344312, France; Foropon, C., Montpellier Business School, Montpellier Research in Management, 2300 Avenue des Moulins, Montpellier34185, France; Roubaud, D., Montpellier Business School, Montpellier Research in Management, 2300 Avenue des Moulins, Montpellier34185, France; Hazen, B.T., Logistikum, University of Applied Sciences, Upper Austria Wehrgrabengasse 1-3, Steyr, 4400, Austria","The importance of big data analytics, artificial intelligence, and machine learning has been at the forefront of research for operations and supply chain management. Literature has reported the influence of big data analytics for improved operational performance, but there has been a paucity of research regarding the role of entrepreneurial orientation (EO) on the adoption of big data analytics. To address this gap, we draw on the dynamic capabilities view of firms and on contingency theory to develop and test a model that describes the role of EO on the adoption of big data analytics powered by artificial intelligence (BDA-AI) and operational performance (OP). We tested our research hypotheses using a survey of 256 responses gathered using a pre-tested questionnaire from manufacturing firms in India with the help of the National Association of Software and Services Companies (NASSCOM) and the Federation of Indian Chambers of Commerce and Industry (FICCI). The results from our analysis indicate that EO enables an organisation to exploit and further explore the BDA-AI capabilities to achieve superior OP. Further, our results provide empirical evidence based on data analysis that EO is strongly associated with higher order capabilities (such as BDA-AI) and OP under differential effects of environmental dynamism (ED). These findings extend the dynamic capability view and contingency theory to create better understanding of dynamic capabilities of the organisation while also providing theoretically grounded guidance to the managers to align their EO with their technological capabilities within their firms. © 2019","Artificial intelligence; Big data analytics; Entrepreneurial orientation; Operational performance; PLS SEM; Supply chain management","Advanced Analytics; Artificial intelligence; Big data; Enterprise resource management; Industrial research; Manufacture; Software testing; Supply chain management; Surveys; Differential effect; Dynamic capabilities; Entrepreneurial orientation; Environmental dynamisms; Manufacturing organisations; Operational performance; Operations and supply chain managements; Technological capability; Data Analytics",,,,,,,,,,,,"Dubey, R.; Montpellier Business School, 2300 Avenue des Moulins, Montpellier, France; email: r.dubey@montpellier-bs.com",,"Elsevier B.V.",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85077155060
"Lezoche M., Panetto H., Kacprzyk J., Hernandez J.E., Alemany Díaz M.M.E.","34977078700;6508186303;26643457100;34975053700;15841436200;","Agri-food 4.0: A survey of the Supply Chains and Technologies for the Future Agriculture",2020,"Computers in Industry","117",,"103187","","",,162,"10.1016/j.compind.2020.103187","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079089114&doi=10.1016%2fj.compind.2020.103187&partnerID=40&md5=99867b4aa801deb113920483f2a3d2d5","University of Lorraine, CRAN/Université de Lorraine, Campus Scientifique BP 70239, Vandoeuvre-les-Nancy, 54506, France","Lezoche, M., University of Lorraine, CRAN/Université de Lorraine, Campus Scientifique BP 70239, Vandoeuvre-les-Nancy, 54506, France; Panetto, H., University of Lorraine, CRAN/Université de Lorraine, Campus Scientifique BP 70239, Vandoeuvre-les-Nancy, 54506, France; Kacprzyk, J., University of Lorraine, CRAN/Université de Lorraine, Campus Scientifique BP 70239, Vandoeuvre-les-Nancy, 54506, France; Hernandez, J.E., University of Lorraine, CRAN/Université de Lorraine, Campus Scientifique BP 70239, Vandoeuvre-les-Nancy, 54506, France; Alemany Díaz, M.M.E., University of Lorraine, CRAN/Université de Lorraine, Campus Scientifique BP 70239, Vandoeuvre-les-Nancy, 54506, France","The term “Agri-Food 4.0” is an analogy to the term ""Industry 4.0"", coming from the concept “agriculture 4.0”. Since the origins of the industrial revolution, where the steam engines started the concept of Industry 1.0 and later the use of electricity upgraded the concept to Industry 2.0, the use of technologies generated a milestone in the industry revolution by addressing the Industry 3.0 concept. Hence, Industry 4.0, it is about including and integrating the latest developments based on digital technologies as well as the interoperability process across them. This allows enterprises to transmit real-time information in terms behaviour and performance. Therefore, the challenge is to maintain these complex networked structures efficiently linked and organised within the use of such technologies, especially to identify and satisfy supply chain stakeholders dynamic requirements. In this context, the agriculture domain is not an exception although it possesses some specialities depending from the domain. In fact, all agricultural machinery incorporates electronic controls and has entered to the digital age, enhancing their current performance. In addition, electronics, using sensors and drones, support the data collection of several agriculture key aspects, such as weather, geographical spatialization, animals and crops behaviours, as well as the entire farm life cycle. However, the use of the right methods and methodologies for enhancing agriculture supply chains performance is still a challenge, thus the concept of Industry 4.0 has evolved and adapted to agriculture 4.0 in order analyse the behaviours and performance in this specific domain. Thus, the question mark on how agriculture 4.0 support a better supply chain decision-making process, or how can help to save time to farmer to make effective decision based on objective data, remains open. Therefore, in this survey, a review of more than hundred papers on new technologies and the new available supply chains methods are analysed and contrasted to understand the future paths of the Agri-Food domain. © 2020","Agri-Food 4.0; Agriculture 4.0; Artificial Intelligence; Big Data; Blockchain; Internet of Things; Supply Chains","Artificial intelligence; Big data; Blockchain; Decision making; Industry 4.0; Internet of things; Life cycle; Supply chains; Surveys; Agri-Food 4.0; Current performance; Decision making process; Digital technologies; Electronic controls; Industrial revolutions; Networked structures; Real-time information; Agriculture","Horizon 2020 Framework Programme, H2020: 691249; European Commission, EC: H2020-MSCA-RISE-2015","Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS “Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems” ( www.ruc-aps.eu ), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.",,,,,,,,,,"Panetto, H.; University of Lorraine, Campus Scientifique BP 70239, France; email: herve.panetto@univ-lorraine.fr",,"Elsevier B.V.",01663615,,CINUD,,"English","Comput Ind",Review,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85079089114
"Thürer M., Tomašević I., Stevenson M., Blome C., Melnyk S., Chan H.K., Huang G.Q.","35811259700;33568407600;35077237500;35793637100;7007031097;55520514300;7403425048;","A systematic review of China’s belt and road initiative: implications for global supply chain management",2020,"International Journal of Production Research","58","8",,"2436","2453",,32,"10.1080/00207543.2019.1605225","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064575273&doi=10.1080%2f00207543.2019.1605225&partnerID=40&md5=b4aabc518ba0d2c0aaba6f819e616fdc","School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, China; Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia; Department of Management Science, Lancaster University Management School, Lancaster University, Lancaster, United Kingdom; BMEc, University of Sussex, Falmer, United Kingdom; Center for Operations Research and Econometrics, UCLouvain, Louvain-la-Neuve, Belgium; Eli Broad School of Business, Michigan State University, East Lansing, MI, United States; Nottingham University Business School China, University of Nottingham Ningbo China, Ningbo, China; Department of Industrial Manufacturing Systems Engineering, University of Hong Kong, Hong Kong; Institute of Physical Internet, Jinan University, Zhuhai, China","Thürer, M., School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, China; Tomašević, I., Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia; Stevenson, M., Department of Management Science, Lancaster University Management School, Lancaster University, Lancaster, United Kingdom; Blome, C., BMEc, University of Sussex, Falmer, United Kingdom, Center for Operations Research and Econometrics, UCLouvain, Louvain-la-Neuve, Belgium; Melnyk, S., Eli Broad School of Business, Michigan State University, East Lansing, MI, United States; Chan, H.K., Nottingham University Business School China, University of Nottingham Ningbo China, Ningbo, China; Huang, G.Q., Department of Industrial Manufacturing Systems Engineering, University of Hong Kong, Hong Kong, Institute of Physical Internet, Jinan University, Zhuhai, China","China’s Belt and Road Initiative (BRI) is one of the world’s largest infrastructure projects, with its potential political and economic impact being widely discussed since its inception in 2013. Yet the phenomenon has received only limited attention in the Supply Chain Management (SCM) literature. In response, we first conduct a broad systematic review of the literature to assess how China’s BRI is portrayed. Using this as a backdrop, we then distil the likely impact of the BRI on location decisions and supply chain flows. Finally, in a broader discussion of the SCM literature, we explore the implications of the BRI for future research in four key areas: supply chain configuration, supply chain resilience, sustainable SCM, and cross border SCM. While these areas are not new, the BRI presents a unique context that can be used to enhance theory and understanding in each area. The BRI reduces time distance independent of geographical distance by diverting supply chain flows from established routes to new routes via far less accessible regions. This introduces new risks and sustainability issues that call for multi-criteria decision support systems. Another important issue is the adoption and diffusion of the BRI since this will ultimately determine project success. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.","Belt and Road Initiative (BRI); global manufacturing; global supply chain; supply chain design; supply chain management","Artificial intelligence; Decision support systems; Geographical regions; Highway administration; Roads and streets; Global manufacturing; Global supply chain; Global supply chain management; Multi-criteria decision support systems; Supply chain configuration; Supply chain design; Supply chain managements (SCM); Supply chain resiliences; Supply chain management","National Natural Science Foundation of China, NSFC: 71750410694, 71872072; Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded Scheme","This work was supported by National Natural Science Foundation of China [grant numbers 71750410694, 71872072]; Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme 2017.",,,,,,,,,,"Thürer, M.; School of Intelligent Systems Science and Engineering, China; email: matthiasthurer@workloadcontrol.com",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85064575273
"Ren H., Zhou W., Guo Y., Huang L., Liu Y., Yu Y., Hong L., Ma T.","56739771400;26539073600;34770667100;57195869314;56509394100;56143801400;57211963547;55786524500;","A GIS-based green supply chain model for assessing the effects of carbon price uncertainty on plastic recycling",2020,"International Journal of Production Research","58","6",,"1705","1723",,11,"10.1080/00207543.2019.1693656","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075494852&doi=10.1080%2f00207543.2019.1693656&partnerID=40&md5=a5f000a655e15e23750e5e9fb7013722","School of Business, East China University of Science and Technology, Shanghai, China; Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway; School of Energy Science and Engineering, Central South University, Changsha, China; Zhejiang Development and Planning Institute, Hangzhou, China; International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria","Ren, H., School of Business, East China University of Science and Technology, Shanghai, China; Zhou, W., Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway; Guo, Y., School of Energy Science and Engineering, Central South University, Changsha, China; Huang, L., Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway; Liu, Y., Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway; Yu, Y., School of Business, East China University of Science and Technology, Shanghai, China; Hong, L., Zhejiang Development and Planning Institute, Hangzhou, China; Ma, T., School of Business, East China University of Science and Technology, Shanghai, China, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria","Recycling plastic can abate the environmental pollution as well as CO2 emissions by saving the carbon-intensive feedstock input. The uncertain carbon price places significant effects on the establishment and operation of the whole supply chain. This study develops a green supply chain model combined with geographic information system (GIS) to account for carbon price uncertainty and evaluate its effects on the closed-loop supply chain (CLSC) of plastic recycling. A two-stage stochastic programming model is constructed, in which the stochastic variable, CO2 price is modelled as a geometric Brownian motion process. Six scenarios are designed with respect to price expectation and volatility. A case study is performed with the GIS information of the plastic supply chain in Zhejiang province, China. The results illustrate that triggering the establishment of reverse logistics requires a carbon price threshold significantly beyond the current level. Lower price volatility would facilitate the decision-making of investment into the reverse logistics. Mechanisms to alleviate the market variation shall be introduced. A sound market condition is desired to obtain the optimal balance that encourages the CLSC without creating extra pressure on the firms. The proposed modelling framework can be easily applied to other sectors with similar characteristics. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.","circular economy; decision support systems; emissions trading; green supply chain; reverse logistics; uncertainty","Artificial intelligence; Brownian movement; Carbon; Carbon dioxide; Commerce; Decision making; Decision support systems; Geographic information systems; Investments; Stochastic models; Stochastic programming; Stochastic systems; Supply chains; Uncertainty analysis; Circular economy; Emissions Trading; Green supply chain; Reverse logistics; uncertainty; Plastic recycling","2013RS4051; National Natural Science Foundation of China, NSFC: 71571069, 71704055, 71874055, 71961137012; National Office for Philosophy and Social Sciences, NSSFC: 19BGL273","The authors thank the financial support from the National Natural Science Foundation of China [grant numbers 71961137012, 71571069, 71704055, 71874055], the National Social Science Fund of China [grant number 19BGL273] and the Scientific Project of Hunan Province of China [grant number 2013RS4051].",,,,,,,,,,"Zhou, W.; Department of Manufacturing and Civil Engineering, Norway; email: wenji.zhou@ntnu.no",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85075494852
"Flores H., Villalobos J.R.","55524240800;7006617652;","A stochastic planning framework for the discovery of complementary, agricultural systems",2020,"European Journal of Operational Research","280","2",,"707","729",,8,"10.1016/j.ejor.2019.07.053","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070232829&doi=10.1016%2fj.ejor.2019.07.053&partnerID=40&md5=9af96c6e7a3bb919c502690c1755ea68","International Logistics and Productivity Improvement Laboratory, Arizona State University, 900 S McAllister Ave, Tempe, AZ  85287, United States","Flores, H., International Logistics and Productivity Improvement Laboratory, Arizona State University, 900 S McAllister Ave, Tempe, AZ  85287, United States; Villalobos, J.R., International Logistics and Productivity Improvement Laboratory, Arizona State University, 900 S McAllister Ave, Tempe, AZ  85287, United States","One of the greatest 21st century challenges is meeting the need to feed a growing world population which is expected to increase by about 35% by 2050. To meet this challenge, it is necessary to make major improvements on current food production and distribution systems capabilities, as well as to adapt these systems to expected trends such as climate change. Changing climate patterns may present opportunities for unidentified, geographical regions with adequate climate patterns to produce high-value agricultural products in a profitable and sustainable manner. This paper focuses on the design and planning aspects of a discovery process to unearth agri-food supply chains capable of generating attractive return on investments. A stochastic optimization framework is used to develop planting and harvesting schedules for a set of identified regions with complementary weather characteristics. To address the high-level of variability in the problem context, a two-stage stochastic decomposition method is used to consider a larger number of scenarios. As part of the solution process, a modeling scheme is developed that learns past interactions between entering discretized, weather scenarios and optimal first-stage solutions. In this context, machine learning and dimensionality reduction techniques are used to iteratively estimate each region's probability of belonging to first-stage solutions based on previous solution-scenario results. The implementation of the stochastic framework is shown through a case study applied to multiple locations within the US southwest states of Arizona and New Mexico. © 2019 Elsevier B.V.","Decision support systems; OR in agriculture; Stochastic decomposition algorithm; Supply chain management; Two-stage stochastic model","Agricultural products; Artificial intelligence; Climate change; Decision support systems; Food supply; Geographical regions; Investments; Iterative methods; Optimization; Stochastic systems; Supply chain management; Agri-food supply chains; Dimensionality reduction techniques; Distribution systems; OR in agriculture; Stochastic decomposition; Stochastic framework; Stochastic optimizations; Two-stage stochastic models; Stochastic models",,,,,,,,,,,,"Villalobos, J.R.; International Logistics and Productivity Improvement Laboratory, 900 S McAllister Ave, United States; email: rene.villalobos@asu.edu",,"Elsevier B.V.",03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","",Scopus,2-s2.0-85070232829
"Fowler J.W., Kim S.-H., Shunk D.L.","7402371012;55376624500;6701636484;","Design for customer responsiveness: Decision support system for push–pull supply chains with multiple demand fulfillment points",2019,"Decision Support Systems","123",,"113071","","",,11,"10.1016/j.dss.2019.113071","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067194635&doi=10.1016%2fj.dss.2019.113071&partnerID=40&md5=e78b7fa88b1d042cbac3fa1a642b1e4b","Department of Supply Chain Management, W. P. Carey School of Business, Arizona State University, Tempe, AZ  85287-5906, United States; Department of Business Administration, Ajou University, Suwon, 16499, South Korea; School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ  85287-5906, United States","Fowler, J.W., Department of Supply Chain Management, W. P. Carey School of Business, Arizona State University, Tempe, AZ  85287-5906, United States; Kim, S.-H., Department of Business Administration, Ajou University, Suwon, 16499, South Korea; Shunk, D.L., School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ  85287-5906, United States","A push–pull supply chain is a hybrid of “push” and “pull” supply chains where semi-finished products are produced by forecasts and “pushed” to a stock point, then are “pulled” by actual customer orders and go through remaining processes to be delivered. In order to design an effective push–pull supply chain, there are two critical issues in the decision making process: how to support decision makers to identify feasible locations for demand fulfillment points based on the product, process, and organizational form of a given enterprise, and how to support decision makers to improve the customer lead time management capability of a push–pull supply chain. In this study, we present a decision support system for designing a push–pull supply chain that 1) incorporates the product, process, and organizational form of a given enterprise; 2) leverages a new hybrid push–pull control model, which enhances the customer lead time management capability; and 3) provides a decision support model that supports decision makers for performing scenario-based analysis in designing a push–pull supply chain. The numerical analysis exhibits how the proposed system can be implemented in the context of a semiconductor supply chain, and subsequently shows that our control model results in substantial improvement of customer lead time management capability over conventional push–pull supply chain designs without a significant inventory cost increase. Also, some experimental results are provided to support decision makers on how to make the transition from the conventional design to the proposed one. © 2019 Elsevier B.V.","Customer Lead time management; Decision support system; Push–pull control; Supply chain design","Artificial intelligence; Decision support systems; Product design; Sales; Supply chain management; Supply chains; Customer responsiveness; Decision making process; Decision support models; Leadtime; Pull control; Scenario-based analysis; Semiconductor supply chain; Supply chain design; Decision making",,,,,,,,,,,,"Kim, S.-H.; Department of Business Administration, South Korea; email: seunk@ajou.ac.kr",,"Elsevier B.V.",01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-85067194635
"Priore P., Ponte B., Rosillo R., de la Fuente D.","6701465358;56029133100;54910269800;7004113754;","Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments",2019,"International Journal of Production Research","57","11",,"3663","3677",,38,"10.1080/00207543.2018.1552369","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058171214&doi=10.1080%2f00207543.2018.1552369&partnerID=40&md5=4d8ad4acbbc117b201f9506fbb843986","Department of Business Administration, Polytechnic School of Engineering, University of Oviedo, Gijón, Spain; Department for People and Organisations, The Open University Business School, The Open University, Milton Keynes, United Kingdom","Priore, P., Department of Business Administration, Polytechnic School of Engineering, University of Oviedo, Gijón, Spain; Ponte, B., Department for People and Organisations, The Open University Business School, The Open University, Milton Keynes, United Kingdom; Rosillo, R., Department of Business Administration, Polytechnic School of Engineering, University of Oviedo, Gijón, Spain; de la Fuente, D., Department of Business Administration, Polytechnic School of Engineering, University of Oviedo, Gijón, Spain","Firms currently operate in highly competitive scenarios, where the environmental conditions evolve over time. Many factors intervene simultaneously and their hard-to-interpret interactions throughout the supply chain greatly complicate decision-making. The complexity clearly manifests itself in the field of inventory management, in which determining the optimal replenishment rule often becomes an intractable problem. This paper applies machine learning to help managers understand these complex scenarios and better manage the inventory flow. Building on a dynamic framework, we employ an inductive learning algorithm for setting the most appropriate replenishment policy over time by reacting to the environmental changes. This approach proves to be effective in a three-echelon supply chain where the scenario is defined by seven variables (cost structure, demand variability, three lead times, and two partners’ inventory policy). Considering four alternatives, the algorithm determines the best replenishment rule around 88% of the time. This leads to a noticeable reduction of operating costs against static alternatives. Interestingly, we observe that the nodes are much more sensitive to inventory decisions in the lower echelons than in the upper echelons of the supply chain. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.","Bullwhip Effect; inductive learning; inventory management; machine learning; replenishment policy; supply chain management","Artificial intelligence; Decision making; Inventory control; Learning systems; Operating costs; Supply chain management; Bullwhip effects; Demand variability; Environmental change; Environmental conditions; Inductive learning; Inventory decisions; Inventory management; Replenishment policy; Learning algorithms",,,,,,,,,,,,"Ponte, B.; Department for People and Organisations, United Kingdom; email: borja.ponte-blanco@open.ac.uk",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85058171214
"Puche J., Costas J., Ponte B., Pino R., de la Fuente D.","45561439400;57062332100;56029133100;7006690508;7004113754;","The effect of supply chain noise on the financial performance of Kanban and Drum-Buffer-Rope: An agent-based perspective",2019,"Expert Systems with Applications","120",,,"87","102",,10,"10.1016/j.eswa.2018.11.009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056798763&doi=10.1016%2fj.eswa.2018.11.009&partnerID=40&md5=0b5c19323b40cae0f06c15d700dfd5da","Department of Applied Economics, Faculty of Economics and Business, University of Burgos, Plaza Infanta Doña Elena s/n, Burgos, 09001, Spain; Department of Engineering, Florida Centre de Formació, Florida Universitària, Rei en Jaume I, n° 2, 46470, Catarroja, Valencia, Spain; Department for People and Organisations, The Open University Business School, The Open University, Walton Hall, D2, Milton Keynes, MK7 6AA, United Kingdom; Department of Business Administration, Polytechnic School of Engineering, University of Oviedo, Campus de Viesques s/n, Gijón, 33204, Spain","Puche, J., Department of Applied Economics, Faculty of Economics and Business, University of Burgos, Plaza Infanta Doña Elena s/n, Burgos, 09001, Spain; Costas, J., Department of Engineering, Florida Centre de Formació, Florida Universitària, Rei en Jaume I, n° 2, 46470, Catarroja, Valencia, Spain; Ponte, B., Department for People and Organisations, The Open University Business School, The Open University, Walton Hall, D2, Milton Keynes, MK7 6AA, United Kingdom; Pino, R., Department of Business Administration, Polytechnic School of Engineering, University of Oviedo, Campus de Viesques s/n, Gijón, 33204, Spain; de la Fuente, D., Department of Business Administration, Polytechnic School of Engineering, University of Oviedo, Campus de Viesques s/n, Gijón, 33204, Spain","Managing efficiently the flow of products throughout the supply chain is essential for succeeding in today's marketplace. We consider the Kanban (from Lean Management) and Drum-Buffer-Rope (DBR, from the Theory of Constraints) scheduling mechanisms and evaluate their performance in a four-echelon supply chain operating within a large noise scenario. Through an agent-based system, which is presented as a powerful model-driven decision support system for managers, we show the lower sensitivity against variability and the higher financial performance of DBR, which occurs as this mechanism improves the supply chain robustness due to its bottleneck orientation. Nonetheless, we prove the existence of regions in the decision space where Kanban offers similar performance. This is especially relevant taking into account that Kanban can be implemented at a lower cost, as DBR requires a higher degree of information transparency and a solid contract between partners to align incentives. In this sense, we offer decision makers a methodological approach to reach an agreement when the partners decide to move from Kanban to DBR in a bid to increase the overall net profit in supply chains operating in a challenging noise scenario. © 2018 Elsevier Ltd","Agent-based modeling and simulation; Drum-Buffer-Rope; Kanban; Lean Management; Supply chain collaboration; Theory of constraints","Artificial intelligence; Autonomous agents; Computational methods; Decision making; Decision support systems; Lean production; Rope; Scheduling; Supply chains; Agent-based modeling and simulation; Drum-buffer-rope; Kanban; Lean management; Supply chain collaboration; Theory of constraint; Supply chain management",,,,,,,,,,,,"Puche, J.; Department of Applied Economics, Plaza Infanta Doña Elena s/n, Spain; email: jcpuche@ubu.es",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85056798763
"Baryannis G., Validi S., Dani S., Antoniou G.","35092202700;55954182300;7005295598;7005674407;","Supply chain risk management and artificial intelligence: state of the art and future research directions",2019,"International Journal of Production Research","57","7",,"2179","2202",,202,"10.1080/00207543.2018.1530476","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063877082&doi=10.1080%2f00207543.2018.1530476&partnerID=40&md5=a7ce5347b562bf60312d70e79e2fccfa","Department of Computer Science, School of Computing & Engineering, University of Huddersfield, Huddersfield, United Kingdom; Department of Logistics, Operations, Hospitality and Marketing, Huddersfield Business School, University of Huddersfield, Huddersfield, United Kingdom","Baryannis, G., Department of Computer Science, School of Computing & Engineering, University of Huddersfield, Huddersfield, United Kingdom; Validi, S., Department of Logistics, Operations, Hospitality and Marketing, Huddersfield Business School, University of Huddersfield, Huddersfield, United Kingdom; Dani, S., Department of Logistics, Operations, Hospitality and Marketing, Huddersfield Business School, University of Huddersfield, Huddersfield, United Kingdom; Antoniou, G., Department of Computer Science, School of Computing & Engineering, University of Huddersfield, Huddersfield, United Kingdom","Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision-making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for artificial intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.","artificial intelligence; decision-making; SCRM strategy; supply chain disruption; supply chain risk management","Artificial intelligence; Data Analytics; Decision making; Learning systems; Mathematical programming; Risk assessment; Risk management; Supply chains; Adaptive decision making; Comprehensive analysis; Future research directions; Multidimensional data; SCRM strategy; Supply chain risk management; Supply chain risk management (SCRM); Supply-chain disruptions; Supply chain management",,,,,,,,,,,,"Baryannis, G.; Department of Computer Science, United Kingdom; email: g.bargiannis@hud.ac.uk",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Review,"Final","All Open Access, Green",Scopus,2-s2.0-85063877082
"Schätter F., Hansen O., Wiens M., Schultmann F.","56313332500;56940000800;56318731100;6602564223;","A decision support methodology for a disaster-caused business continuity management",2019,"Decision Support Systems","118",,,"10","20",,23,"10.1016/j.dss.2018.12.006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059127654&doi=10.1016%2fj.dss.2018.12.006&partnerID=40&md5=c595fa75dc3b788e6c2d2d68b882ff04","Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), Hertzstraße 16, Karlsruhe, 76187, Germany; Kühne Logistics University (KLU), Großer Grasbrook 17, Hamburg, 20457, Germany","Schätter, F., Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), Hertzstraße 16, Karlsruhe, 76187, Germany; Hansen, O., Kühne Logistics University (KLU), Großer Grasbrook 17, Hamburg, 20457, Germany; Wiens, M., Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), Hertzstraße 16, Karlsruhe, 76187, Germany; Schultmann, F., Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), Hertzstraße 16, Karlsruhe, 76187, Germany","Supply chain risk management typically deals with the systematic identification, analysis and mitigation of risks which affect the whole supply chain network of a company. Business continuity management (BCM) forms part of supply chain risk management and is an important competitive factor for companies by ensuring the smooth functioning of critical business processes in the case of failures. If business operations are severely disrupted, the companies’ decision maker is confronted with a situation which is characterized by a high degree of uncertainty, complexity and time pressure. In such a context, decision support can be of significant value. This article presents a novel decision support methodology which leads to an improved and more robust BCM for severe disruptions caused by disasters. The methodology is part of the Reactive Disaster and supply chain Risk decision Support System (ReDRiSS) to deal with different levels of information availability and to provide decision makers with a robust decision recommendation regarding resource allocation problems. It combines scenario techniques, optimization models and approaches from decision theory to operate in an environment characterized by sparse or lacking information and dynamic changes over time. A simulation case study is presented where the methodology is applied within the BCM of a food retail company in Berlin that is affected by a pandemic disaster. © 2018 Elsevier B.V.","Business continuity management; Decision support system; Disaster management; Risk management; Robust decision-making","Artificial intelligence; Decision support systems; Decision theory; Disaster prevention; Disasters; Risk assessment; Risk management; Supply chain management; Supply chains; Business continuity management; Degree of uncertainty; Disaster management; Information availability; Resource allocation problem; Robust decisions; Supply chain risk management; Systematic identification; Decision making","Bundesministerium für Bildung und Forschung, BMBF","We would like to thank the German Federal Ministry of Education and Research (BMBF) for financial support for this work within the research project SEAK.",,,,,,,,,,"Schätter, F.; Karlsruhe Institute of Technology (KIT), Hertzstraße 16, Germany; email: frank.schaetter@kit.edu",,"Elsevier B.V.",01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-85059127654
"J.-Sharahi S., Khalili-Damghani K.","57204037352;36806209600;","Fuzzy type-II De-Novo programming for resource allocation and target setting in network data envelopment analysis: A natural gas supply chain",2019,"Expert Systems with Applications","117",,,"312","329",,19,"10.1016/j.eswa.2018.09.046","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054185039&doi=10.1016%2fj.eswa.2018.09.046&partnerID=40&md5=fff33b71f38c16cbb28d92dbd1013be4","Department of Industrial Engineering, South Tehran Branch, Islamic Azad UniversityTehran, Iran","J.-Sharahi, S., Department of Industrial Engineering, South Tehran Branch, Islamic Azad UniversityTehran, Iran; Khalili-Damghani, K., Department of Industrial Engineering, South Tehran Branch, Islamic Azad UniversityTehran, Iran","Developing effective approaches to design optimal resources of system based on the concepts of benchmark in DEA and optimal design in De-Novo programming is one of the important managerial decision making problems. In this paper, a decision support system is developed for allocation of resources and setting the targets across a set of entities in an equitable manner in presence of uncertainty. The proposed approach has two main modules. First, the most suitable system is designed using De-Novo programming. De-Novo programming. De-Novo programming is used to optimally determine the inputs (i.e., resources) and outputs (i.e., targets) of DMUs in network DEA rather than optimizing existing DMUs. Then, the optimal values of resources are allocated and optimal values of the targets are set in a complex network structure. Furthermore, in real-world problems budget of resources and targets are usually mixed with uncertainties, so in this paper, two concept of fuzzy and interval type-II fuzzy resources and target are developed for resource allocation and target setting. Finally numerical example based on real case of natural gas supply chain is also used to evaluate the applicability and efficacy of the proposed models. © 2018 Elsevier Ltd","Data envelopment analysis; De-Novo programming; Natural gas supply chain; Resource allocation and target setting; Type-I 129fuzzy optimization; Type-II fuzzy optimization","Artificial intelligence; Budget control; Complex networks; Data envelopment analysis; Decision making; Decision support systems; Gas supply; Natural gas; Optimal systems; Resource allocation; Supply chains; Effective approaches; Fuzzy optimization; Managerial decision making; Network structures; Novo programming; Optimal values; Real-world problem; Target setting; Natural gas deposits","National Iranian Gas Company, NIGC: 950438","This research has been accomplished on the basis of a PhD dissertation by Sara J.-Sharahi supervised by Prof. Kaveh Khalili-Damghani at Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran. This research has been supported by National Iranian Gas Company (NIGC) under the contract number 950438. The authors wold like to thank you the reviewers and editor for their insightful comments.",,,,,,,,,,"Khalili-Damghani, K.; Department of Industrial Engineering, Iran; email: k_khalili@azad.ac.ir",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-85054185039
"Zhao K., Scheibe K., Blackhurst J., Kumar A.","36635367700;7003542782;6507178682;56169882200;","Supply Chain Network Robustness Against Disruptions: Topological Analysis, Measurement, and Optimization",2019,"IEEE Transactions on Engineering Management","66","1","8329409","127","139",,27,"10.1109/TEM.2018.2808331","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044785291&doi=10.1109%2fTEM.2018.2808331&partnerID=40&md5=35cc23da44d131e1ab7cd2cbf22581be","Management Sciences Department, Henry B. Tippie College of Business, University of Iowa, Iowa City, IA  52242-1994, United States; Supply Chain and Information Systems Department, Iowa State University, Ames, IA  50011, United States; Smeal College of Business, Pennsylvania State University, University Park, PA  16801, United States","Zhao, K., Management Sciences Department, Henry B. Tippie College of Business, University of Iowa, Iowa City, IA  52242-1994, United States; Scheibe, K., Supply Chain and Information Systems Department, Iowa State University, Ames, IA  50011, United States; Blackhurst, J., Management Sciences Department, Henry B. Tippie College of Business, University of Iowa, Iowa City, IA  52242-1994, United States; Kumar, A., Smeal College of Business, Pennsylvania State University, University Park, PA  16801, United States","This paper focuses on understanding the robustness of a supply network in the face of a disruption. We propose a decision support system for analyzing the robustness of supply chain networks against disruptions using topological analysis, performance measurement relevant to a supply chain context, and an optimization for increasing supply network performance. The topology of a supply chain network has considerable implications for its robustness in the presence of disruptions. The system allows decision makers to evaluate topologies of their supply chain networks in a variety of disruption scenarios, thereby proactively managing the supply chain network to understand vulnerabilities of the network before a disruption occurs. Our system calculates performance measurements for a supply chain network in the face of disruptions and provides both topological metrics (through network analysis) and operational metrics (through an optimization model). Through an example application, we evaluate the impact of random and targeted disruptions on the robustness of a supply chain network. © 1988-2012 IEEE.","Decision support; disruption; optimization; robustness; simulation; supply chain network topology","Artificial intelligence; Decision making; Decision support systems; Optimization; Robustness (control systems); Supply chains; Decision supports; disruption; Optimization modeling; Performance measurements; simulation; Supply chain network; Topological analysis; Topological metrics; Topology","Sun Microsystems; Instytut Biologii Medycznej Polskiej Akademii Nauk; National Science Foundation","Dr. Kumar has been a Principal Investigator for the National Science Foundation and also received support from IBM, Sun Microsystems, and other organizations for his work. He is an Associate Editor for the ACM Transactions on Management Information Systems.",,,,,,,,,,"Blackhurst, J.; Management Sciences Department, United States; email: jennifer-blackhurst@uiowa.edu",,"Institute of Electrical and Electronics Engineers Inc.",00189391,,IEEMA,,"English","IEEE Trans Eng Manage",Article,"Final","",Scopus,2-s2.0-85044785291
"Kellner F., Lienland B., Utz S.","54410576800;55773874000;55098733100;","An a posteriori decision support methodology for solving the multi-criteria supplier selection problem",2019,"European Journal of Operational Research","272","2",,"505","522",,39,"10.1016/j.ejor.2018.06.044","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050853706&doi=10.1016%2fj.ejor.2018.06.044&partnerID=40&md5=6b51e359e1bbff0045e4e99e85f5fd1d","Faculty of Business, Economics and Management Information Systems, University of Regensburg, Germany; School of Finance, Institute for Operations Research and Computational Finance, University of St.Gallen, Switzerland","Kellner, F., Faculty of Business, Economics and Management Information Systems, University of Regensburg, Germany; Lienland, B., Faculty of Business, Economics and Management Information Systems, University of Regensburg, Germany; Utz, S., School of Finance, Institute for Operations Research and Computational Finance, University of St.Gallen, Switzerland","This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate ‘risk’ into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting. © 2018 Elsevier B.V.","Decision support systems; Logistics; Supplier selection; Supply chain management; Sustainability","Artificial intelligence; Computation theory; Decision support systems; Economic and social effects; Electronic trading; Investments; Logistics; Multiobjective optimization; Pareto principle; Sales; Supply chain management; Sustainable development; Analytic network process; Investment portfolio; Multi-objective optimization models; Optimization modeling; Portfolio selection problems; Quadratic objective functions; Supplier selection; Sustainability performance; Decision making",,,,,,,,,,,,"Kellner, F.; Faculty of Business, Germany; email: florian.kellner@ur.de",,"Elsevier B.V.",03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85050853706
"Azzamouri A., Bara N., Elfirdoussi S., Essaadi I., Fontane F., Giard V.","57191034149;57211455514;55360329200;57191044026;36604260400;23974468200;","DSS approach for heterogeneous parallel machines scheduling considering proximate supply chain constraints",2019,"International Journal of Production Research",,,,"","",,1,"10.1080/00207543.2019.1661539","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074045084&doi=10.1080%2f00207543.2019.1661539&partnerID=40&md5=69d97648c2273ce8624f9ce0f4809e25","EMINES–School of Industrial Management, Mohammed VI Polytechnic University, Ben Guerir, Morocco; Mines-Paris Tech, PSL Research University, Paris, France; Paris-Dauphine, PSL Research University, Paris, France","Azzamouri, A., EMINES–School of Industrial Management, Mohammed VI Polytechnic University, Ben Guerir, Morocco; Bara, N., EMINES–School of Industrial Management, Mohammed VI Polytechnic University, Ben Guerir, Morocco; Elfirdoussi, S., EMINES–School of Industrial Management, Mohammed VI Polytechnic University, Ben Guerir, Morocco; Essaadi, I., EMINES–School of Industrial Management, Mohammed VI Polytechnic University, Ben Guerir, Morocco; Fontane, F., EMINES–School of Industrial Management, Mohammed VI Polytechnic University, Ben Guerir, Morocco, Mines-Paris Tech, PSL Research University, Paris, France; Giard, V., EMINES–School of Industrial Management, Mohammed VI Polytechnic University, Ben Guerir, Morocco, Paris-Dauphine, PSL Research University, Paris, France","This paper describes the basis of a Decision Support System (DSS) designed to schedule fertiliser production orders to be delivered within time windows, in plants made up of multiple heterogeneous parallel processors (production lines), considering that fertiliser production rates and nomenclatures depend on lines, that setup times depend on sequence and lines, and taking into account downtime constraints (preventive maintenance …). A mixed linear programming model is encapsulated in the DSS which considers the schedule’s impacts, immediately upstream and downstream of plants in the supply chain. These side-effects may make the proposed solution unfeasible and the DSS helps redefining the problem to avoid them. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.","decision support system; heterogeneous parallel processors; non-availability constraints; optimisation; scheduling; sequence dependent setup; supply chain","Artificial intelligence; Fertilizers; Linear programming; Parallel processing systems; Preventive maintenance; Scheduling; Supply chains; Availability constraints; Decision support system (dss); Mixed linear programming; Optimisations; Parallel machines scheduling; Parallel processor; Production rates; Sequence dependent setups; Decision support systems",,,,,,,,,,,,"Giard, V.; EMINES–School of Industrial Management, Morocco; email: vincent.giard@dauphine.fr",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","",Scopus,2-s2.0-85074045084
"Hong J., Diabat A., Panicker V.V., Rajagopalan S.","23004982100;26646404900;54946804400;57204317235;","A two-stage supply chain problem with fixed costs: An ant colony optimization approach",2018,"International Journal of Production Economics","204",,,"214","226",,20,"10.1016/j.ijpe.2018.07.019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053142278&doi=10.1016%2fj.ijpe.2018.07.019&partnerID=40&md5=13613e4e6ba7e2472405b5cbbd9da9f7","International Business School, Shanghai University of International Business and Economics, 1900 Wenxiang Rd., Songjiang District, Shanghai, 201620, China; Division of Engineering, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi, 129188, United Arab Emirates; Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, NY  11201, United States; Department of Mechanical Engineering, National Institute of Technology CalicutKerala  673601, India","Hong, J., International Business School, Shanghai University of International Business and Economics, 1900 Wenxiang Rd., Songjiang District, Shanghai, 201620, China; Diabat, A., Division of Engineering, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi, 129188, United Arab Emirates, Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, NY  11201, United States; Panicker, V.V., Department of Mechanical Engineering, National Institute of Technology CalicutKerala  673601, India; Rajagopalan, S., Department of Mechanical Engineering, National Institute of Technology CalicutKerala  673601, India","This paper focuses on a distribution-allocation problem in a two-stage supply chain with fixed costs. The problem is intended to determine a supply chain configuration of manufacturing plants, distributors, and retailers in the distribution network. The problem is formulated as an integer-programming model. The mathematical model incorporates unit transportation costs between entities and two types of fixed costs, including fixed cost for transportation routes and fixed cost for opening facilities. The objective of the model is to minimise the total costs of supply chain operation incurred in allocating the retailers to a distribution centre and the distribution centres to a manufacturing plant. An Ant Colony Optimization (ACO)-based heuristic is developed for solving the model. The heuristic is tested on various problem sizes generated. All the problem instances are solved using solver LINGO to evaluate the robustness of the ACO-based algorithm. The ACO-based heuristic emerges as a computationally efficient algorithm. Solutions can be obtained using the ACO-based heuristic within a reasonable computational time with a gap of about 10% on average from the optimal solutions. © 2018 Elsevier B.V.","Ant colony optimization; Distribution-allocation; Fixed charge transportation problem; Supply chain","Artificial intelligence; Cost accounting; Costs; Integer programming; Manufacture; Stages; Supply chains; Transportation routes; Warehouses; Ant Colony Optimization (ACO); Computationally efficient; Distribution-allocation; Fixed charge transportation; Integer programming models; Supply chain configuration; Supply chain operation; Two-stage supply chains; Ant colony optimization",,,,,,,,,,,,"Diabat, A.; Division of Engineering, Saadiyat Island, United Arab Emirates; email: diabat@nyu.edu",,"Elsevier B.V.",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-85053142278
"Villegas M.A., Pedregal D.J.","57098851600;6507216906;","Supply chain decision support systems based on a novel hierarchical forecasting approach",2018,"Decision Support Systems","114",,,"29","36",,14,"10.1016/j.dss.2018.08.003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051630317&doi=10.1016%2fj.dss.2018.08.003&partnerID=40&md5=9a9be47d0f967c647167d36e8b1fe185","ETSI Industriales., University of Castilla-La Mancha, Ciudad Real, 13071, Spain","Villegas, M.A., ETSI Industriales., University of Castilla-La Mancha, Ciudad Real, 13071, Spain; Pedregal, D.J., ETSI Industriales., University of Castilla-La Mancha, Ciudad Real, 13071, Spain","Time series forecasting plays an important role in many decision support systems, also in those related to the management of supply chains. Forecast accuracy is, therefore, essential to optimise the efficiency of any supply chain. One aspect that is often overlooked is the fact that sales of many products within an organization are assembled as complex hierarchies with different levels of aggregation. Very often forecasts are produced regardless of such structure, though forecasting accuracy may be improved by taking it into account. In this paper, an approach for hierarchical time series forecasting based on State Space modelling is proposed. Previous developments provide solutions to the hierarchical forecasting problem by algebra manipulations based on forecasts produced by independent models for each time series involved in the hierarchy. The solutions produce optimal reconciled forecasts for each individual forecast horizon, but the link along time that is implied by the dynamics of the models is completely ignored. Therefore, the novel approach in this paper improves upon past research at least in two key points. Firstly, the algebra is already encoded in the State Space system and the Kalman Filter algorithm, giving an elegant and clean solution to the problem. Secondly, the State Space approach is optimal both across the hierarchy, as expected, but also along time, something missing in past developments. The approach is assessed by comparing its forecasting performance to the existing methods, through simulations and using real data of a Spanish grocery retailer. © 2018","Decision support system; Forecasting; Hierarchical forecasting; Reconciliation; State Space","Algebra; Artificial intelligence; Forecasting; Hierarchical systems; State space methods; Supply chains; Time series; Forecasting accuracy; Forecasting performance; Forecasting problems; Kalman filter algorithms; Management of supply chains; Reconciliation; State space approach; Time series forecasting; Decision support systems","DPI2015-64133-R; Universidad de Castilla-La Mancha, UCLM: 2014/10340; European Regional Development Fund, FEDER","This work was supported by the European Regional Development Fund and Spanish Government (MINECO/FEDER, UE) under the project with reference DPI2015-64133-R and by the Vicerrectorado de Investigación y Política Científica from UCLM by DOCM 31/07/2014 [2014/10340] . We also would like to thank two anonymous referees for their valuable comments.",,,,,,,,,,"Pedregal, D.J.; ETSI Industriales., Spain; email: diego.pedregal@uclm.es",,"Elsevier B.V.",01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85051630317
"Papagiannidis S., See-To E.W.K., Assimakopoulos D.G., Yang Y.","8848778400;16403237500;21833416400;57213785579;","Identifying industrial clusters with a novel big-data methodology: Are SIC codes (not) fit for purpose in the Internet age?",2018,"Computers and Operations Research","98",,,"355","366",,16,"10.1016/j.cor.2017.06.010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021329944&doi=10.1016%2fj.cor.2017.06.010&partnerID=40&md5=797434e55845c7b638636d97610f69ff","Business School, Newcastle University, 5 Barack Road, Newcastle upon Tyne, NE1 4SE, United Kingdom; Department of Computing and Decision Sciences, Faculty of Business, Lingnan University, Tuen Mun, Hong Kong; Department of Economics, Finance and Control, EMLYON Business School, 23, avenue Guy de Collongue, Ecully Cedex, CS40203 69134, France; Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong","Papagiannidis, S., Business School, Newcastle University, 5 Barack Road, Newcastle upon Tyne, NE1 4SE, United Kingdom; See-To, E.W.K., Department of Computing and Decision Sciences, Faculty of Business, Lingnan University, Tuen Mun, Hong Kong; Assimakopoulos, D.G., Department of Economics, Finance and Control, EMLYON Business School, 23, avenue Guy de Collongue, Ecully Cedex, CS40203 69134, France; Yang, Y., Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong","In this paper we propose using a novel big-data-mining methodology and the Internet as a new source of useful meta-data for industry classification. The proposed methodology can be utilised as a decision support system for identifying industrial clusters in almost real time in a specific geographic region, contributing to strategic co-operation and policy development for operations and supply chain management across organisational boundaries through big data analytics. Our theoretical discussion on discerning industrial activity of firms in geographical regions starts by highlighting the limitations of the Standard Industrial Classification (SIC) codes. This discussion is followed by the proposed methodology, which has three main steps revolving around web-based data collection, pre-processing and analysis, and reporting of clusters. We discuss each step in detail, presenting the experimental approaches tested. We apply our methodology to a regional case, in the North East of England, in order to demonstrate how such a big data decision support system/analytics can work in practice. Implications for theory, policy and practice are discussed, as well as potential avenues for further research. © 2017 Elsevier Ltd","Big data analytics; Clusters; Industry classification; North East of England; Operations; Regional policy; SIC codes; Strategic co-operation","Artificial intelligence; Classification (of information); Codes (symbols); Data mining; Decision support systems; Geographical regions; Information management; Supply chain management; Big Data Analytics; Clusters; England; Industry classification; Operations; Regional policies; SIC codes; Strategic co-operation; Big data",,,,,,,,,,,,"Papagiannidis, S.; Business School, 5 Barack Road, United Kingdom; email: savvas.papagiannidis@ncl.ac.uk",,"Elsevier Ltd",03050548,,CMORA,,"English","Comp. Oper. Res.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85021329944
"Gupta N., Dutta G., Tiwari M.K.","56167199200;7101813811;35427952100;","An integrated decision support system for strategic supply chain optimisation in process industries: the case of a zinc company",2018,"International Journal of Production Research","56","17",,"5866","5882",,5,"10.1080/00207543.2018.1456698","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046008763&doi=10.1080%2f00207543.2018.1456698&partnerID=40&md5=d82bee754ea39e004c46806c34e88328","Operations Management, Management Development Institute, Gurgaon, India; Production and Quantitative Methods, Indian Institute of Management, Ahmedabad, India; Department of Industrial and Systems Engineering, Indian Institute of Technology, Kharagpur, India","Gupta, N., Operations Management, Management Development Institute, Gurgaon, India; Dutta, G., Production and Quantitative Methods, Indian Institute of Management, Ahmedabad, India; Tiwari, M.K., Department of Industrial and Systems Engineering, Indian Institute of Technology, Kharagpur, India","We introduce a menu-driven user-friendly decision support system (DSS) for supply chain planning based on optimisation. The DSS is based on a multi-source (supplier), multi-destination (warehouse) network having multiple manufacturing facilities, with multiple materials and multiple storage areas. This integrated supply chain model performs multiple period planning. The use of this DSS requires little knowledge of management sciences tools. We discuss the need for an integrated approach towards supply chain modelling for the process industry. We present the integrated model in the form of a database structure. We validate the model with the real data of a zinc company and demonstrate the impact of optimisation in terms of percentage improvement. The result shows that it is possible to improve unit contribution to profit from 1.89 to 4.66%. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.","decision support system; linear programming; manufacturing systems; optimisation; process industry; supply chain management","Artificial intelligence; Digital storage; Linear programming; Manufacture; Supply chain management; Zinc; Decision support system (dss); Integrated supply chain model; Manufacturing facility; Optimisations; Process industries; Supply chain modelling; Supply chain optimisation; Supply chain planning; Decision support systems",,,,,,,,,,,,"Dutta, G.; Production and Quantitative Methods, India; email: goutam@iima.ac.in",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-85046008763
"Hombach L.E., Büsing C., Walther G.","56543639700;36459470200;22942752600;","Robust and sustainable supply chains under market uncertainties and different risk attitudes – A case study of the German biodiesel market",2018,"European Journal of Operational Research","269","1",,"302","312",,30,"10.1016/j.ejor.2017.07.015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026781148&doi=10.1016%2fj.ejor.2017.07.015&partnerID=40&md5=7301c930a5b4aec25975dc05d58ddd6d","RWTH Aachen University, Chair of Operations Management, Kackertstraße 7, Aachen, D-52072, Germany; RWTH Aachen University, Chair II of Mathematik, Pontdriesch 12-14, Aachen, D-52062, Germany","Hombach, L.E., RWTH Aachen University, Chair of Operations Management, Kackertstraße 7, Aachen, D-52072, Germany; Büsing, C., RWTH Aachen University, Chair II of Mathematik, Pontdriesch 12-14, Aachen, D-52062, Germany; Walther, G., RWTH Aachen University, Chair of Operations Management, Kackertstraße 7, Aachen, D-52072, Germany","The transportation sector emits 22% of the global CO2 emissions, 75% of them resulting from road transportation. The European Union aims to reduce these emissions, which can be achieved by blending biofuels into fossil fuels. To obtain robust and sustainable biofuel supply chains, political regulations need to simultaneously combine ecologic and social aspects with economic considerations, known as the triple-bottom-line dimensions of sustainability. Next to these conflicting sustainability objectives, uncertain planning parameters as well as the decision makers’ different risk attitudes must be taken into account to obtain robust and sustainable biofuel supply chains. In this paper, we develop a robust, multi-objective approach to solve this uncertain and multi-objective supply chain problem. For this purpose, the decision maker's risk attitude is integrated in the design of the scenario sets modeling the uncertainties. This model is applied to the German biodiesel market. We show that a trade-off between the three sustainability targets exists, analyze in detail the relation of the used scenario sets and the decision maker's risk attitude, and show how the selected risk attitude influences the sustainability performance of the biofuel supply chain. © 2017 Elsevier B.V.","Biomass-to-biodiesel; Decision support systems; Multi-objective optimization; Robust optimization; Sustainable","Artificial intelligence; Biodiesel; Biofuels; Blending; Decision support systems; Economic and social effects; Fossil fuels; Multiobjective optimization; Optimization; Risk perception; Social aspects; Supply chains; Sustainable development; Uncertainty analysis; Biofuel supply chains; Economic considerations; Robust optimization; Sustainability objectives; Sustainability performance; Sustainable; Sustainable supply chains; Transportation sector; Decision making",,,,,,,,,,,,"Hombach, L.E.; RWTH Aachen University, Kackertstraße 7, Germany; email: laura.hombach@om.rwth-aachen.de",,"Elsevier B.V.",03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","",Scopus,2-s2.0-85026781148
"Wang X., Choi T.-M., Liu H., Yue X.","55995743900;7202769936;57111517600;15838243100;","A novel hybrid ant colony optimization algorithm for emergency transportation problems during post-disaster scenarios",2018,"IEEE Transactions on Systems, Man, and Cybernetics: Systems","48","4",,"545","556",,54,"10.1109/TSMC.2016.2606440","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038017926&doi=10.1109%2fTSMC.2016.2606440&partnerID=40&md5=ae352377d3d1d7989074efba6bbfdbd6","Department of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, 116025, China; Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hong Kong, Hong Kong; Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI  53201, United States","Wang, X., Department of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, 116025, China; Choi, T.-M., Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hong Kong, Hong Kong; Liu, H., Department of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, 116025, China; Yue, X., Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI  53201, United States","The increasing impacts of natural disasters have led to concerns regarding predisaster plans and post-disaster responses. During post-disaster responses, emergency transportation is the most important part of disaster relief supply chain operations, and its optimal planning differs from traditional transportation problems in the objective function and complex constraints. In disaster scenarios, fairness and effectiveness are two important aspects. This paper investigates emergency transportation in real-life disasters scenarios and formulates the problem as an integer linear programming model (called cum-MDVRP), which combines cumulative vehicle routing problem and multidepot vehicle routing problem. The cum-MDVRP is NP-hard. To solve it, a novel hybrid ant colony optimization-based algorithm is proposed by combining both saving algorithms and a simple two-step 2-opt algorithm. The proposed algorithm allows ants to go in and out the depots for multiple rounds, so we abbreviate it as ACOMR. Moreover, we present a smart design of the ants' tabus, which helps to simplify the solution constructing process. The ACOMR could yield good solutions quickly, then the decision makers for emergency responses could do expert planning at the earliest time. Computational results on standard benchmarking data sets show that the proposed cum-MDVRP model performs well, and the ACOMR algorithm is more effective and stable than the existing algorithms. © 2013 IEEE.","Ant colony optimization (ACO); cumulative multidepot vehicle routing problem (cum-MDVRP); emergency transportation; fairness and efficiency; integer linear programming","Ant colony optimization; Artificial intelligence; Civil defense; Decision making; Disaster prevention; Disasters; Integer programming; Supply chains; Vehicle routing; Vehicles; Ant Colony Optimization (ACO); Hybrid ant colony optimization; Integer Linear Programming; Integer linear programming models; Multi-depot vehicle routing problems; Supply chain operation; Transportation problem; Vehicle Routing Problems; Optimization","National Natural Science Foundation of China, NSFC: 61402086, 71110107024, 71501032; Department of Education of Liaoning Province: L2015165; Dongbei University of Finance and Economics, DUFE: DUFE2015R06","Manuscript received July 1, 2016; accepted August 31, 2016. Date of publication October 19, 2016; date of current version March 15, 2018. This work was supported in part by the National Natural Science Foundation of China under Grant 61402086 and Grant 71501032, in part by the International Cooperation and Exchange of the National Natural Science Foundation of China under Grant 71110107024, in part by the Research Project of Education Department of Liaoning Province under Grant L2015165, and in part by the Outstanding Talent Project of the Dongbei University of Finance and Economics under Grant DUFE2015R06. This paper was recommended by Associate Editor J.-H. Chou. (Corresponding author: Tsan-Ming Choi.) X. Wang and H. Liu are with the Department of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 116025, China (e-mail: Distribute_2008@163.com; sea_liuhaikuo@163.com).",,,,,,,,,,"Choi, T.-M.; Institute of Textiles and Clothing, Hong Kong; email: jason.choi@polyu.edu.hk",,"Institute of Electrical and Electronics Engineers Inc.",21682216,,,,"English","IEEE Trans. Syst. Man Cybern. Syst.",Article,"Final","",Scopus,2-s2.0-85038017926
"Dellino G., Laudadio T., Mari R., Mastronardi N., Meloni C.","24172417800;6507921005;56888578100;55964268400;7005098320;","A reliable decision support system for fresh food supply chain management",2018,"International Journal of Production Research","56","4",,"1458","1485",,30,"10.1080/00207543.2017.1367106","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028536634&doi=10.1080%2f00207543.2017.1367106&partnerID=40&md5=c6775707cfa222615dddf9d274f511ca","Istituto per le Applicazioni del Calcolo ‘M. Picone’, Consiglio Nazionale delle Ricerche, Bari, Italy; Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, Bari, Italy","Dellino, G., Istituto per le Applicazioni del Calcolo ‘M. Picone’, Consiglio Nazionale delle Ricerche, Bari, Italy, Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, Bari, Italy; Laudadio, T., Istituto per le Applicazioni del Calcolo ‘M. Picone’, Consiglio Nazionale delle Ricerche, Bari, Italy; Mari, R., Istituto per le Applicazioni del Calcolo ‘M. Picone’, Consiglio Nazionale delle Ricerche, Bari, Italy; Mastronardi, N., Istituto per le Applicazioni del Calcolo ‘M. Picone’, Consiglio Nazionale delle Ricerche, Bari, Italy; Meloni, C., Istituto per le Applicazioni del Calcolo ‘M. Picone’, Consiglio Nazionale delle Ricerche, Bari, Italy, Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, Bari, Italy","The paper proposes a decision support system (DSS) for the supply chain of packaged fresh and highly perishable products. The DSS combines a unique tool for sales forecasting with order planning which includes an individual model selection system equipped with ARIMA, ARIMAX and transfer function forecasting model families, the latter two accounting for the impact of prices. Forecasting model parameters are chosen via two alternative tuning algorithms: a two-step statistical analysis, and a sequential parameter optimisation framework for automatic parameter tuning. The DSS selects the model to apply according to user-defined performance criteria. Then, it considers sales forecasting as a proxy of expected demand and uses it as input for a multi-objective optimisation algorithm that defines a set of non-dominated order proposals with respect to outdating, shortage, freshness of products and residual stock. A set of real data and a benchmark–based on the methods already in use–are employed to evaluate the performance of the proposed DSS. The analysis of different configurations shows that the DSS is suitable for the problem under investigation; in particular, the DSS ensures acceptable forecasting errors and proper computational effort, providing order plans with associated satisfactory performances. © 2017 Informa UK Limited, trading as Taylor & Francis Group.","decision support systems; forecasting; fresh food supply chain; optimisation; order proposal","Artificial intelligence; Benchmarking; Food supply; Forecasting; Multiobjective optimization; Optimization; Sales; Supply chain management; Automatic parameter tuning; Computational effort; Decision support system (dss); Fresh food; Optimisations; order proposal; Parameter optimisation; Performance criterion; Decision support systems","2012MTE38N, POR FESR 2007-2013","This work was supported by the E-CEDI project, funded by Apulia Region under the POR FESR 2007-2013 grant, Asse I, Linea 1.2 - Azione 1.2.4. The work of the second author was partly supported by PRIN 2012 n. 2012MTE38N.",,,,,,,,,,"Dellino, G.; Istituto per le Applicazioni del Calcolo ‘M. Picone’, Italy; email: gabriella.dellino@poliba.it",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-85028536634
"Simchi-Levi D., Wu M.X.","7004509983;57196151485;","Powering retailers’ digitization through analytics and automation",2018,"International Journal of Production Research","56","1-2",,"809","816",,16,"10.1080/00207543.2017.1404161","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035087514&doi=10.1080%2f00207543.2017.1404161&partnerID=40&md5=3652554dc3df5ee3fd61a00e18258244","Operations Research Center, Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, MA, United States; School of Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States; Carson College of Business, Washington State University, Pullman, WA, United States","Simchi-Levi, D., Operations Research Center, Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, MA, United States, School of Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States; Wu, M.X., Carson College of Business, Washington State University, Pullman, WA, United States","Retailers face significant pressure to improve revenue, margins and market share by applying price optimisation models. These are mathematical models that calculate how demand varies at different price levels, then combine that data with information on costs and inventory levels to recommend prices that will improve revenue and profits. These models have been around for a while-so what is different now? We have identified three important changes: (1) Data: availability of internal and external real-time data such as traffic to a website, consumers making buy/no buy decisions and competitor pricing strategies; (2) Analytics: advances in machine learning and ease of access (R, Python) have enabled the development of systems that learn on the fly about consumer behaviour and preferences and generate effective estimates of demand-price relationships; and (3) Automation: increase in computing speed enables real-time optimisation of prices of hundreds of competing products sold by the same retailer. We take advantage of these new opportunities by showing how they were applied at Boston-based flash sales retailer Rue La La, online market maker Groupon, and the largest online retailer in Latin America, B2W Digital (B2W). While all these examples are of on-line businesses which have readily available data and can change prices dynamically, we have also implemented similar methods for brick-and-mortar retailed in applications such as promotional pricing, new product introduction, and assortment optimisation with similar business impacts. Thus, beyond applications to price optimisations, these new trends enable companies to revolutionise their business from procurement to supply chain all the way to revenue management. © 2017 Informa UK Limited, trading as Taylor & Francis Group.","analytics; forecasting; machine learning; online retail; price theory","Artificial intelligence; Competition; Computation theory; Economics; Electronic commerce; Forecasting; Learning systems; Optimization; Real time systems; Sales; Supply chains; analytics; Inventory levels; New product introductions; Online retails; Price optimisation; Price theory; Pricing strategy; Revenue management; Costs","Higher Education Funding Council for England, HEFCE; Royal Society; Particle Physics and Astronomy Research Council, PPARC","I would like to thank K. Baskerville, P. Ferreira, and K. Gorski for help with this project. Special thanks to T. Banday for supplying DMR maps without the eclipse season data. This work was performed on COSMOS, the Origin 2000 supercomputer owned by the UK-CCC, and supported by HEFCE and PPARC. I thank the Royal Society for financial support.",,,,,,,,,,"Simchi-Levi, D.; Operations Research Center, United States; email: dslevi@mit.edu",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85035087514
"Ma H., Wang Y., Wang K.","57193409584;53982348500;55501467600;","Automatic detection of false positive RFID readings using machine learning algorithms",2018,"Expert Systems with Applications","91",,,"442","451",,42,"10.1016/j.eswa.2017.09.021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029489992&doi=10.1016%2fj.eswa.2017.09.021&partnerID=40&md5=330aa10e4b9a268753a2f7a356018754","Norwegian University of Science and Technology, Department of Production and Quality Engineering, S.P. Andersens veg 5, Trondheim, 7031, Norway; The university of Manchester, School of Materials, Sackville St Blg, Manchester, M13 9PL, United Kingdom","Ma, H., Norwegian University of Science and Technology, Department of Production and Quality Engineering, S.P. Andersens veg 5, Trondheim, 7031, Norway; Wang, Y., The university of Manchester, School of Materials, Sackville St Blg, Manchester, M13 9PL, United Kingdom; Wang, K., Norwegian University of Science and Technology, Department of Production and Quality Engineering, S.P. Andersens veg 5, Trondheim, 7031, Norway","Radio frequency identification (RFID) has been widely used for the automatic identification, tracking and tracing of goods throughout the supply chain from the manufacturer to the customer. However, one technological problem that impedes the productive and reliable use of RFID is the constraint of false positive readings, which refers to tags that are detected accidentally by the reader but not the ones of interest. This paper focuses on the use of machine learning algorithms to identify such RFID readings. A total of 11 statistical features are extracted from received signal strength (RSS) and phase rotations derived from the raw RFID data. Each of the features is highly statistically different to distinguish the false positive readings, but satisfactory classification cannot be achieved when these features are considered individually. Classifiers based on logistic regression (LR), support vector machine (SVM) and decision tree (DT) are constructed, which combine all of the extracted features to classify the RFID readings more effectively. The performance of the classifiers is evaluated in a real-world factory. Results show that SVM provides the highest accuracy of up to 95.3%. DT shows slightly better accuracy (92.85%) than LR (92.75%), while LR has the larger area under the curve (0.976) than DT (0.949). Overall, machine learning algorithms could achieve accuracy of 93% on average. The proposed methodology provides a much more reliable RFID application as false-positive readings are detected immediately without human intervention, which enables a significant potential of fully automatic identification and tracking of goods throughout the supply chain. © 2017 Elsevier Ltd","Classification; False positive readings; Machine learning; RFID","Artificial intelligence; Automation; Classification (of information); Data mining; Decision trees; Learning systems; Radio frequency identification (RFID); Supply chains; Support vector machines; Area under the curves; Automatic Detection; Automatic identification; False positive; Logistic regressions; Received signal strength; Statistical features; Tracking and tracing; Learning algorithms","201406890022","The authors are grateful to the Technical Editor and all Reviewers for their valuable and constructive comments. The research is supported by the China Scholar Council (CSC) under Grant no. 201406890022 .",,,,,,,,,,"Wang, K.; Norwegian University of Science and Technology, S.P. Andersens veg 5, Norway; email: kesheng.wang@ntnu.no",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85029489992
"Kannan D.","35558819200;","Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process",2018,"International Journal of Production Economics","195",,,"391","418",,158,"10.1016/j.ijpe.2017.02.020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021973076&doi=10.1016%2fj.ijpe.2017.02.020&partnerID=40&md5=a01e433ec299588d003de330f1360531","Center for Sustainable Supply Chain Engineering, Department of Technology and Innovation, University of Southern Denmark, Odense M, Denmark","Kannan, D., Center for Sustainable Supply Chain Engineering, Department of Technology and Innovation, University of Southern Denmark, Odense M, Denmark","The concept of sustainability has become an essential theme for many industries and organisations due to a heightened sensitivity towards environmental protection and social responsibility. At the same time, firms still need to achieve economic viability and retain their competitive advantage. Because suppliers are the first entity and the initial source of any supply chain, organisations should select their suppliers by a careful evaluation of their critical success factors (CSF). Achieving a successful sustainable supply chain management (SSCM) strategy requires a firm to consider its stakeholders’ views. Based on the CSF theory and by considering the multi-stakeholders’ view in a sustainability perspective, this work provides a decision support system for the sustainable supplier selection (SSS) problem in a real world textile industry located in the emerging economy of India. Through a three-phase methodology, this study examines Indian suppliers by considering the sustainability views of various stakeholders, including employees, customers, researchers, shareholders, and a government environmental officer. The CSF priorities show that the first four influential CSFs are categorized as social concerns (i.e., maintaining long-term relationships and alliances, stakeholder empowerment, equity labour sources, and individual human rights). The fifth factor is an environmental issue (i.e., production of polluting agents). Among the five suppliers being evaluated in this work, Supplier 4 receives the top ranking. Specifically, the results show that the supplier rankings are highly influenced by CSF's social dimensions. Hence, to validate the influence of CSF's social dimensions in relation to the SSS process, a sensitivity analysis has been done by varying the respective weights. The study concludes with the relevant managerial implications and limitations. © 2017 Elsevier B.V.","ANP; COPRAS-G; Critical success factor; Fuzzy Delphi; ISM; Multi criteria decision making; Stakeholder theory; Sustainable supplier selection; Triple bottom line","Artificial intelligence; Competition; Decision support systems; Decision theory; Economic and social effects; Enterprise resource planning; Environmental protection; Industrial economics; Sensitivity analysis; Supply chain management; Textile industry; COPRAS-G; Critical success factor; Fuzzy Delphi; Multi criteria decision making; Stakeholder theory; Sustainable supplier selections; Triple Bottom Line; Sustainable development",,,,,,,,,,,,"Kannan, D.; Center for Sustainable Supply Chain Engineering, Denmark; email: deka@iti.sdu.dk",,"Elsevier B.V.",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-85021973076
"Yazdani M., Zarate P., Coulibaly A., Zavadskas E.K.","56908500300;6602655724;57209016131;6602254601;","A group decision making support system in logistics and supply chain management",2017,"Expert Systems with Applications","88",,,"376","392",,79,"10.1016/j.eswa.2017.07.014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024856023&doi=10.1016%2fj.eswa.2017.07.014&partnerID=40&md5=84393eb83dae0a33bda4ff0260b897f9","University of Toulouse, Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse, France; Research Institute of Smart Building Technologies, Vilnius Gediminas Technical University, Lithuania","Yazdani, M., University of Toulouse, Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse, France; Zarate, P., University of Toulouse, Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse, France; Coulibaly, A., University of Toulouse, Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse, France; Zavadskas, E.K., Research Institute of Smart Building Technologies, Vilnius Gediminas Technical University, Lithuania","Purpose The paper proposes a decision support system for selecting logistics providers based on the quality function deployment (QFD) and the technique for order preference by the similarity to ideal solution (TOPSIS) for agricultural supply chain in France. The research provides a platform for group decision making to facilitate decision process and check the consistency of the outcomes. Methodology The proposed model looks at the decision problem from two points of view considering both technical and customer perspectives. The main customer criteria are confidence in a safe and durable product, emission of pollutants and hazardous materials, social responsibility, etc. The main technical factors are financial stability, quality, delivery condition, services, etc. based on the literature review. The second stage in the adopted methodology is the combination of quality function deployment and the technique for order preference by similarity to ideal solution to effectively analyze the decision problem. In final section we structure a group decision system called GRoUp System (GRUS) which has been developed by Institut de Recherche en Informatique de Toulouse (IRIT) in the Toulouse University. Results This paper designs a group decision making system to interface decision makers and customer values in order to aid agricultural partners and investors in the selection of third party logistic providers. Moreover, we have figured out a decision support system under fuzzy linguistic variables is able to assist agricultural parties in uncertain situations. This integrated and efficient decision support system enhances quality and reliability of the decision making. Novelty/Originality The novelty of this paper is reflected by several items. The integration of group multi-criteria decision tools enables decision makers to obtain a comprehensive understanding of customer needs and technical requirements of the logistic process. In addition, this investigation is carried out under a European commission project called Risk and Uncertain Conditions for Agriculture Production Systems (RUC-APS) which models risk reduction and elimination from the agricultural supply chain. Ultimately, we have implemented the decision support tool to select the best logistic provider among France logistics and transportation companies. © 2017 Elsevier Ltd","Fuzzy linguistic variables; Group decision support system; Logistic provider; Quality function deployment; Supply chain; Technique for order preference by similarity to ideal solution","Agriculture; Artificial intelligence; Decision support systems; Decision theory; Hazardous materials; Linguistics; Quality function deployment; Sales; Stability criteria; Supply chain management; Supply chains; Agricultural supply chains; European Commission projects; Fuzzy linguistic variable; Group decision making support systems; Logistics and supply chain management; Logistics and transportations; Quality function deployments (QFD); Technique for order preference by similarity to ideal solutions; Decision making","Horizon 2020 Framework Programme, H2020: 691249",,,,,,,,,,,"Yazdani, M.; Research Institute of Smart Building Technologies, Lithuania; email: morteza.yazdani@irit.fr",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85024856023
"Wruck S., Vis I.F.A., Boter J.","55862885400;6507007157;8667905800;","Risk control for staff planning in e-commerce warehouses",2017,"International Journal of Production Research","55","21",,"6453","6469",,13,"10.1080/00207543.2016.1207816","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978731962&doi=10.1080%2f00207543.2016.1207816&partnerID=40&md5=17333385a1f29927b39455f3e44374d2","Faculty of Economics and Business Administration, Department of Marketing, VU University Amsterdam, Amsterdam, Netherlands; Faculty of Economics and Business, Department of Operations, University of Groningen, Groningen, Netherlands","Wruck, S., Faculty of Economics and Business Administration, Department of Marketing, VU University Amsterdam, Amsterdam, Netherlands; Vis, I.F.A., Faculty of Economics and Business, Department of Operations, University of Groningen, Groningen, Netherlands; Boter, J., Faculty of Economics and Business Administration, Department of Marketing, VU University Amsterdam, Amsterdam, Netherlands","Internet sale supply chains often need to fulfil quickly small orders for many customers. The resulting high demand and planning uncertainties pose new challenges for e-commerce warehouse operations. Here, we develop a decision support tool to assist managers in selecting appropriate risk policies and making staff planning decisions in uncertain conditions. Multistage stochastic modelling has been used to analyse risk optimisation approaches and expected value-based optimisation. Exhaustive numerical and practical validations have been performed to test the tool’s applicability. We demonstrate, using a Dutch e-commerce warehouse, that the multi-period conditional value at risk appears to be most applicable. © 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","decision support systems; e-commerce; risk management; staff planning; warehouse design","Artificial intelligence; Commerce; Electronic commerce; Human resource management; Risk assessment; Risk management; Sales; Stochastic control systems; Stochastic systems; Supply chains; Value engineering; Warehouses; Decision support tools; Expected values; Internet sales; Multi-period conditional value at risk; Risk controls; Uncertain condition; Warehouse design; Warehouse operation; Decision support systems",,,,,,,,,,,,"Vis, I.F.A.; Faculty of Economics and Business, Netherlands; email: i.f.a.vis@rug.nl",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-84978731962
"Bogataj D., Bogataj M., Hudoklin D.","16444160900;57131507900;6507122128;","Mitigating risks of perishable products in the cyber-physical systems based on the extended MRP model",2017,"International Journal of Production Economics","193",,,"51","62",,51,"10.1016/j.ijpe.2017.06.028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021821156&doi=10.1016%2fj.ijpe.2017.06.028&partnerID=40&md5=9f645c7d2a73440567cb82acf13015be","Department of Management and Engineering, University of Padua, Italy; CERRISK, Vrtača 9, Ljubljana, Slovenia; University of Ljubljamn, FE, Tržaška c, Ljubljana, Slovenia","Bogataj, D., Department of Management and Engineering, University of Padua, Italy; Bogataj, M., CERRISK, Vrtača 9, Ljubljana, Slovenia; Hudoklin, D., University of Ljubljamn, FE, Tržaška c, Ljubljana, Slovenia","In the supply chain of fresh fruit and vegetables, large losses may incurred throughout the whole farm to fork route. Food supply chain management is faced with challenges of minimizing the post-harvest loss, while delivering the items directly to the refrigerators in smart homes (i.e. domotics). A substantial value can therefore be added to the criterion function by an immediate, real-time detection of changes in perishability dynamics, including a real-time calculation and communication of the remaining shelf life during transportation from one chain node to another. The changes in the estimated remaining shelf life can, therefore, be matched with the expected remaining transportation time, and so the critical moment can be avoided with a given probability. This can be done by dynamic rerouting in real time, based on previous net present value (NPV) criteria. Such criteria could then we include in the contractually stipulated remaining shelf life requirements at the delivery point. This paper focuses on a novel concept of moving activity cells which represent the moving cargo between the fixed activity cells in the extended material requirements planning (EMRP) model. The changes in NPV are calculated dynamically from the expected shelf life changes. Such real-time calculations and early reports are enabled by the Internet of Things (IoT) infrastructure, where there is a smart device that tracks ambient conditions like temperature, humidity, and gas concentrations. These early estimations allow a better decision making based on first-expired-first-out (FEFO) cold chain management strategies for perishable products. Therefore, the model includes the possibility to deliver the items to the local market if the expected contractually stipulated shelf-life losses become too high. The paper does not intend to discuss the details of IoT or analyse different sensors, but it wishes to show how the EMRP theory can be used to estimate the changes in NPV when moving activity cells are included in the model. The smart measurement devices embedded in moving activity cells of cyber-physical system measure the ambient data and broadcast decay acceleration factors and postharvest loss of cargo to the decision support system. The numerical example shows how smart measurement devices embedded in moving activity cells of cyber-physical system help to reduce the post-harvest loss in a supply chain by rerouting when necessary. The paper additionally shows how much such a cyber-physical system improves NPV by the development of decision-making processes in the real-time, using the IoT as infrastructure, including automatic rerouting in postharvest logistics. © 2017 Elsevier B.V.","Collaborative cities; Cyber-physical systems; Food delivery; IoT; Material requirements planning theory; Post-harvest loss prevention; Smart container; Smart home","Artificial intelligence; Automation; Cells; Cyber Physical System; Cytology; Decision support systems; Embedded systems; Flexible manufacturing systems; Food supply; Intelligent buildings; Internet of things; Supply chain management; Collaborative cities; Food delivery; Material requirements planning; Post-harvest loss; Smart homes; Decision making",,,,,,,,,,,,"Bogataj, D.; Department of Management and Engineering, Italy; email: david.bogataj@unipd.it",,"Elsevier B.V.",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-85021821156
"Wanke P., Alvarenga H., Correa H., Hadi-Vencheh A., Azad M.A.K.","16200418700;57194266874;7006328260;55917035100;56494514000;","Fuzzy inference systems and inventory allocation decisions: Exploring the impact of priority rules on total costs and service levels",2017,"Expert Systems with Applications","85",,,"182","193",,20,"10.1016/j.eswa.2017.05.043","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019580841&doi=10.1016%2fj.eswa.2017.05.043&partnerID=40&md5=51ca1f824236c085dcb5d1045006b0e5","COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal Lemme 355, Rio de Janeiro, 21949-900, Brazil; Crummer Graduate School of Business, Rollins College, 1000 Holt Ave. – 2722, Winter Park, Fl  32789, United States; Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran; Department of Business Administration, School of Business, Bangladesh Army International University of Science and Technology, Comilla, 3501, Bangladesh","Wanke, P., COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal Lemme 355, Rio de Janeiro, 21949-900, Brazil; Alvarenga, H., COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal Lemme 355, Rio de Janeiro, 21949-900, Brazil; Correa, H., Crummer Graduate School of Business, Rollins College, 1000 Holt Ave. – 2722, Winter Park, Fl  32789, United States; Hadi-Vencheh, A., Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran; Azad, M.A.K., Department of Business Administration, School of Business, Bangladesh Army International University of Science and Technology, Comilla, 3501, Bangladesh","Inventory allocation decisions in a distribution system concern issues such as how much and where stock should be assigned to orders in a supply chain. When the inventory level of an inventory point is lower than the total number of items ordered by lower echelons in the chain, the decision of how many items to allocate to each ``competing'' order must take into consideration the trade-off between cost and service level. This paper proposes a decision-support system that makes use of fuzzy logic to consider inventory carrying, shortage and ordering costs as well as transportation costs. The proposed system is compared through simulation with three other inventory allocation decision support models in terms of cost and service levels achieved. Conclusions are then drawn. © 2017 Elsevier Ltd","Fuzzy inference; Fuzzy systems; Inventory allocation; Inventory cost; Service level","Artificial intelligence; Costs; Decision support systems; Economic and social effects; Fuzzy logic; Fuzzy systems; Supply chains; Distribution systems; Fuzzy inference systems; Inventory allocation; Inventory costs; Inventory levels; Service levels; Transportation cost; Use of fuzzy logic; Fuzzy inference",,,,,,,,,,,,"Wanke, P.; COPPEAD Graduate Business School, Rua Paschoal Lemme 355, Brazil; email: peter@coppead.ufrj.br",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-85019580841
"Santos L.F.D.O.M., Osiro L., Lima R.H.P.","57193446256;55792742200;57206498529;","A model based on 2-tuple fuzzy linguistic representation and Analytic Hierarchy Process for supplier segmentation using qualitative and quantitative criteria",2017,"Expert Systems with Applications","79",,,"1339","1351",,52,"10.1016/j.eswa.2017.02.032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014120099&doi=10.1016%2fj.eswa.2017.02.032&partnerID=40&md5=fafd873a4814a9b7225d0a3cb2e5de17","Production Engineering Department, University of São Paulo, São Paulo, SP, Brazil; Production Engineering Department, Federal University of Triangulo Mineiro, Uberaba, MG, Brazil; Production Engineering Department, Federal University of Technology Paraná, Londrina, PR, Brazil","Santos, L.F.D.O.M., Production Engineering Department, University of São Paulo, São Paulo, SP, Brazil; Osiro, L., Production Engineering Department, Federal University of Triangulo Mineiro, Uberaba, MG, Brazil; Lima, R.H.P., Production Engineering Department, Federal University of Technology Paraná, Londrina, PR, Brazil","The literature on supply base segmentation has increasingly adopted multi-criteria decision making (MCDM) techniques into recently proposed models. However, most proposals segment the supply base from the standpoint of the purchased item, which prevents them from providing guidelines that are specific to each supplier. Some authors have attempted to overcome these limitations by putting forward portfolio models based on the relationship with suppliers. These approaches use fuzzy variables and MCDM methods that take qualitative judgements by experts as the only input for decision making. However, many companies have databases with historical data about the performance of past transactions with suppliers that should be considered by expert systems that aim to comprehensively evaluate suppliers’ performance. This paper seeks to address this gap by proposing a segmentation model based on the relationship with suppliers capable of aggregating quantitative and qualitative criteria. Analytic Hierarchy Process (AHP) was used to determine the relative importance of each criteria. Fuzzy 2-tuple, a prominent computing with word (CWW) approach, was used to evaluate suppliers with a mixture of historical quantitative data and qualitative judgements by purchasing experts. An illustrative application of the proposed model was carried out in the pharmaceutical supply center (PSC) of a teaching hospital. The proposed model can be viewed as a decision support system capable of aggregating the qualitative judgements of experts and quantitative historical performance measures, thus providing guidelines to improve the relationship between suppliers and the buyer firm. © 2017 Elsevier Ltd","2-tuple linguistic representation model; AHP; Supply chain management; Supply segmentation","Analytic hierarchy process; Artificial intelligence; Decision making; Decision support systems; Expert systems; Fuzzy inference; Hierarchical systems; Hospitals; Linguistics; Sales; Supply chain management; 2-tuple fuzzy linguistic representations; 2-tuple linguistic representation models; Analytic hierarchy process (ahp); Historical performance; Multi-criteria decision making; Quantitative criteria; Relationship with suppliers; Supplier segmentation; Electronic trading",,,,,,,,,,,,"Osiro, L.; Production Engineering Department, Brazil; email: lauro@icte.uftm.edu.br",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-85014120099
"Guarnaschelli A., Bearzotti L., Montt C.","55253449800;6507177788;56644770200;","An approach to export process management in a wood product enterprise",2017,"International Journal of Production Economics","190",,,"88","95",,3,"10.1016/j.ijpe.2016.08.036","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009159813&doi=10.1016%2fj.ijpe.2016.08.036&partnerID=40&md5=7c24d2b4ef0b444fe16319a5627d886c","Escuela de Ingeniería de Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso, 2241, Chile","Guarnaschelli, A., Escuela de Ingeniería de Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso, 2241, Chile; Bearzotti, L., Escuela de Ingeniería de Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso, 2241, Chile; Montt, C., Escuela de Ingeniería de Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso, 2241, Chile","Integration in the context of supply chain management is fundamental for achieving more resilient supply chains. The complexity of export processes challenges integration efforts, augmenting the need for specialized decision support systems to organize and systemize export decisions. The goal of this paper is to introduce an approach for the design of a decision support system that supports the wood product export process of a Chilean enterprise. The approach is focused on minimizing cost and obtaining a better global service level. The different actors involved in the wood export process are studied, with a particular focus on the negotiation process between the wood enterprise and shipping companies. The decision support system relies on an order management model for the logistics management of the enterprise to adequately solve the supplier selection and order allocation problems. The order management model relies on lexicographic goal programming to account for the multi-criteria nature of the underlying decision process. A case study is used to illustrate the benefits of the proposal. © 2016 Elsevier B.V.","Decision support system; Distribution; Order allocation; Supplier selection; Wood supply chain","Artificial intelligence; Decision making; Linear programming; Product design; Supply chain management; Wood; Wood products; Distribution; Lexicographic goal programming; Logistics management; Negotiation process; Order allocation; Shipping companies; Supplier selection; Wood supply; Decision support systems","Pontificia Universidad Católica de Valparaíso, PUCV: 037.295/2015","This work has been done through funding provided by Pontificia Universidad Católica de Valparaíso through its research project funding initiative (Proyecto Código 037.295/2015",,,,,,,,,,"Guarnaschelli, A.; Escuela de Ingeniería de Transporte, Avenida Brasil, Chile; email: armando.guarnaschelli@ucv.cl",,"Elsevier B.V.",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-85009159813
"Werthmann D., Brandwein D., Ruthenbeck C., Scholz-Reiter B., Freitag M.","55858787400;55060858500;35100877300;55060855700;8616327000;","Towards a standardised information exchange within finished vehicle logistics based on RFID and EPCIS",2017,"International Journal of Production Research","55","14",,"4136","4152",,20,"10.1080/00207543.2016.1254354","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994860318&doi=10.1080%2f00207543.2016.1254354&partnerID=40&md5=06cd5c6aee47789ec1ba302e8b915139","BIBA–Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Bremen, Germany; J.H. Tönnjes E.A.S.T GmbH & CO. KG, Delmenhorst, Germany; University of Bremen, Bremen, Germany","Werthmann, D., BIBA–Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Bremen, Germany; Brandwein, D., BIBA–Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Bremen, Germany, J.H. Tönnjes E.A.S.T GmbH & CO. KG, Delmenhorst, Germany; Ruthenbeck, C., BIBA–Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Bremen, Germany; Scholz-Reiter, B., University of Bremen, Bremen, Germany; Freitag, M., BIBA–Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Bremen, Germany, University of Bremen, Bremen, Germany","Finished vehicle logistics is facing different challenges. In order to cope with these challenges this article presents a case study, analysing a concept to improve finished vehicle logistics using radio frequency identification (RFID)-based and electronic product code information services (EPCIS). Within this case study, vehicles were identified automatically by RFID. For sharing, the information generated by RFID the EPCIS-based InfoBroker was developed according to the needs of the automotive industry. By having data available through the InfoBroker, decision support systems (DSS) can make planning and control of finished vehicle logistics decisions more efficiently. The case study was executed within two finished vehicle logistics cases. Based on this article practitioners can estimate the potentials of RFID and EPCIS for finished vehicle logistics. Based on the data made accessible by the presented concept practitioners and researches can develop innovative DSS to improve finished vehicle logistics. © 2016 Informa UK Limited, trading as Taylor & Francis Group.","automotive industry; decision support system; finished vehicle logistics; process automation; radio frequency identification (RFID); supply chain management","Artificial intelligence; Automotive industry; Decision support systems; Identification (control systems); Information dissemination; Information services; Radio waves; Supply chain management; Vehicles; Decision support system (dss); Electronic product codes; Finished vehicles; Information exchanges; Logistics decisions; Planning and control; Process automation; Radio frequency identification (RFID)",,,,,,,,,,,,"Werthmann, D.; BIBA–Bremer Institut für Produktion und Logistik GmbH, Germany; email: wdi@biba.uni-bremen.de",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-84994860318
"Nabelsi V., Gagnon S.","52564054200;56236382400;","Information technology strategy for a patient-oriented, lean, and agile integration of hospital pharmacy and medical equipment supply chains",2017,"International Journal of Production Research","55","14",,"3929","3945",,27,"10.1080/00207543.2016.1218082","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981229289&doi=10.1080%2f00207543.2016.1218082&partnerID=40&md5=7b476ebaef87d06a411d3c3c683a0362","Department of Administrative Sciences, Université du Québec en Outaouais, Gatineau, Canada","Nabelsi, V., Department of Administrative Sciences, Université du Québec en Outaouais, Gatineau, Canada; Gagnon, S., Department of Administrative Sciences, Université du Québec en Outaouais, Gatineau, Canada","Both public and private hospitals are increasingly under pressure to reduce costs while improving patient care across all medical disciplines and departments. Hospitals must become patient-oriented, lean, and agile in order to properly realign and integrate health care processes, helping to reconcile efficiency imperatives with patient needs and hospital mission. One of the highest potential for improvement can be found in supply chain management (SCM) practices for medical supplies, which often represent more than 40% of a hospital’s operating budget. We report on 3 case studies of business process management and reengineering projects, relying on advanced information technology, focused on the supply chains of two major urban hospitals, involving $2 million in minimum stocks for drug inventory. Case study 1 deals with an in-depth analysis of SCM practices around a key medical asset in pharmaceutical supply, i.e. infusion pumps. Case study 2 builds upon the findings of case 1, and proposes an radio-frequency identification solution to support a new hospital-wide asset location process and system, aiming for just-in-time availability of infusion pumps for critical drugs administration. Case study 3 complements cases 1 and 2 by analysing the feasibility of integrating the various components of the hospital pharmacy inventories, which in turn could be integrated to asset location systems. Our 3 case studies lead us to a number of conclusions on how hospitals can develop a patient-oriented, agile, and lean perspectives and practices, as well as ensure the proper integration of patient needs within optimised supply chains. © 2016 Informa UK Limited, trading as Taylor & Francis Group.","business process management (BPM); decision support system (DSS); infusion pumps; Patient-oriented, lean, and agile (POLA) hospitals; pharmacy inventory; supply chain management (SCM)","Administrative data processing; Agile manufacturing systems; Artificial intelligence; Biomedical equipment; Budget control; Decision support systems; Enterprise resource management; Hospitals; Pumps; Radio frequency identification (RFID); Reengineering; Business process management; Decision support system (dss); Infusion pump; Patient-oriented; pharmacy inventory; Supply chain managements (SCM); Supply chain management",,,,,,,,,,,,"Nabelsi, V.; Department of Administrative Sciences, Canada; email: stephane.gagnon@uqo.ca",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-84981229289
"Çimen M., Kirkbride C.","18233525700;7003643044;","Approximate dynamic programming algorithms for multidimensional flexible production-inventory problems",2017,"International Journal of Production Research","55","7",,"2034","2050",,9,"10.1080/00207543.2016.1264643","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85003875629&doi=10.1080%2f00207543.2016.1264643&partnerID=40&md5=93598ff42b5313e721159a11109164cd","Business Administration Department, Hacettepe University, Ankara, Turkey; Department of Management Science, Lancaster University Management School, Lancaster, United Kingdom","Çimen, M., Business Administration Department, Hacettepe University, Ankara, Turkey; Kirkbride, C., Department of Management Science, Lancaster University Management School, Lancaster, United Kingdom","An important issue in the manufacturing and supply chain literature concerns the optimisation of inventory decisions. Single-product inventory problems are widely studied and have been optimally solved under a variety of assumptions and settings. However, as systems become more complex, inventory decisions become more complicated for which the methods/approaches for optimising single inventory systems are incapable of deriving optimal policies. Manufacturing process flexibility provides an example of such a complex application area. Decisions involving the interrelated product inventories and production facilities form a highly multidimensional, non-decomposable system for which optimal policies cannot be readily obtained. We propose the methodology of approximate dynamic programming (ADP) to overcome the computational challenge imposed by this multidimensionality. Incorporating a sample backup simulation approach, ADP develops policies by utilising only a fraction of the computations required by classical dynamic programming. However, there are few studies in the literature that optimise production decisions in a stochastic, multi-factory, multi-product inventory system of this complexity. This paper aims to explore the feasibility and relevancy of ADP algorithms for this application. We present the results from numerical experiments that establish the strong performance of policies developed via temporal difference ADP algorithms in comparison to optimal policies and to policies derived from a deterministic approximation of the problem. © 2016 Informa UK Limited, trading as Taylor & Francis Group.","approximate dynamic programming; dynamic programming; flexible manufacturing; inventory control; machine learning; process flexibility","Approximation algorithms; Artificial intelligence; Flexible manufacturing systems; Inventory control; Learning systems; Manufacture; Optimization; Stochastic systems; Supply chains; Approximate dynamic programming; Computational challenges; Deterministic approximation; Flexible manufacturing; Interrelated products; Manufacturing process; Numerical experiments; Process flexibility; Dynamic programming",,,,,,,,,,,,"Çimen, M.; Business Administration Department, Turkey; email: mcimen@hacettepe.edu.tr",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85003875629
"Xu S., Liu Y., Chen M.","56707845600;36650981800;57192178087;","Optimisation of partial collaborative transportation scheduling in supply chain management with 3PL using ACO",2017,"Expert Systems with Applications","71",,,"173","191",,21,"10.1016/j.eswa.2016.11.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84999738055&doi=10.1016%2fj.eswa.2016.11.016&partnerID=40&md5=99c68b2ae6570f97abb6850b6aa0f427","School of Management, Shenyang University of Technology, Shenyang, Liaoning  110870, China","Xu, S., School of Management, Shenyang University of Technology, Shenyang, Liaoning  110870, China; Liu, Y., School of Management, Shenyang University of Technology, Shenyang, Liaoning  110870, China; Chen, M., School of Management, Shenyang University of Technology, Shenyang, Liaoning  110870, China","In this paper, we study and analyze the characteristics of transportation vehicle scheduling problem in supply chain management with third party logistics enterprise. First, we subdivide all the transportation nodes into three distinct classifications. A novel partial collaborative transportation scheduling strategy is proposed based on two special kinds of transportation nodes which have integrated the self-support vehicle and 3PL vehicle resource. Then, depending on the transport mode of each kind of transportation nodes, a modified Ant Colony Optimization with negative selection operation (ACO-nso) with varying dimension matrix encoding and modified transition probability operation method has been presented. Finally, the simulative results demonstrate that the proposed approach is practical and efficient. © 2016 Elsevier Ltd","ACO-nso; Partial collaborative transportation scheduling; Supply chain management; Transportation vehicle scheduling; Varying dimension matrix encoding","Ant colony optimization; Artificial intelligence; Encoding (symbols); Outsourcing; Scheduling; Transportation; Vehicles; ACO-nso; Matrix encoding; Negative selection; Scheduling strategies; Third party logistics enterprise; Transition probabilities; Transportation vehicles; Vehicle resources; Supply chain management","National Natural Science Foundation of China, NSFC: 70431003; Hebei Province Science and Technology Support Program: 2013216015","This work is supported by the National Natural Science Foundation of China (No. 70431003 ), the Science and Technology Support Program of Liaoning Province (No. 2013216015 ) and the Science and Technology Support Program of Shenyang (No. F13-051-2-00 & No. F14-231-1-24 ).",,,,,,,,,,"Xu, S.; School of Management, China; email: xushida@live.com",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84999738055
"Chang Y., Erera A.L., White C.C.","57195384212;7801316807;7404153613;","Risk Assessment of Deliberate Contamination of Food Production Facilities",2017,"IEEE Transactions on Systems, Man, and Cybernetics: Systems","47","3","7339706","381","393",,12,"10.1109/TSMC.2015.2500822","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014646380&doi=10.1109%2fTSMC.2015.2500822&partnerID=40&md5=315e934756a72410b31d556479500185","School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA  30332, United States","Chang, Y., School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA  30332, United States; Erera, A.L., School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA  30332, United States; White, C.C., School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA  30332, United States","The deliberate contamination of food is well recognized as a major public health threat. A food supply chain offers several possible targets for the intentional insertion of a biological or chemical toxin by a perpetrator, which can result in significant morbidity and mortality. We assume that both manager (defender) of the food production facility and perpetrator (attacker) select actions at each of a possibly countable number of decision epochs, based on possibly inaccurate real-time observations of the other agent. The defender's objectives are to maximize long-run expected total discounted system productivity and to minimize the long-run expected total discounted consequence of an attack. The attacker's objective is to maximize its reward, which combines the long-run expected total discounted consequence of an attack with a penalty if the attack is unsuccessful. We model this problem as a leader-follower, two-agent partially observed Markov game. We show that system risk is dynamic, determine the impact of observation accuracy on facility productivity and risk, thus providing a measure of the value of information, and perform a sensitivity analysis on key parameters. We present an illustrative example involving a liquid egg production system. © 2013 IEEE.","Artificial intelligence; food security; Markov decision process; risk analysis","Artificial intelligence; Food microbiology; Food supply; Health risks; Markov processes; Productivity; Risk analysis; Sensitivity analysis; Supply chains; Chemical toxins; Food production; Food security; Leader-follower; Markov Decision Processes; Real time observation; System productivity; Value of information; Risk assessment","U.S. Department of Homeland Security, DHS: 2010-ST-061-FD0001","This work was supported by the U.S. Department of Homeland Security under Grant 2010-ST-061-FD0001 through a grant from the National Center for Food Protection and Defense at the University of Minnesota.",,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",21682216,,,,"English","IEEE Trans. Syst. Man Cybern. Syst.",Article,"Final","",Scopus,2-s2.0-85014646380
"Zhang X., Chan F.T.S., Adamatzky A., Mahadevan S., Yang H., Zhang Z., Deng Y.","57194857713;7202586517;55152430900;23392894900;7406556890;56948389100;7401531533;","An intelligent physarum solver for supply chain network design under profit maximization and oligopolistic competition",2017,"International Journal of Production Research","55","1",,"244","263",,34,"10.1080/00207543.2016.1203075","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979017491&doi=10.1080%2f00207543.2016.1203075&partnerID=40&md5=0adcb22fc3d8174efff71a0d0182d6a3","School of Computer and Information Science, Southwest University, Chongqing, China; Department of Civil and Environmental Engineering, School of Engineering, Vanderbilt University, Nashville, TN, United States; Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hum, Hong Kong; Unconventional Computing Center, University of the West of England, Bristol, United Kingdom; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong","Zhang, X., School of Computer and Information Science, Southwest University, Chongqing, China, Department of Civil and Environmental Engineering, School of Engineering, Vanderbilt University, Nashville, TN, United States; Chan, F.T.S., Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hum, Hong Kong; Adamatzky, A., Unconventional Computing Center, University of the West of England, Bristol, United Kingdom; Mahadevan, S., Department of Civil and Environmental Engineering, School of Engineering, Vanderbilt University, Nashville, TN, United States; Yang, H., Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong; Zhang, Z., School of Computer and Information Science, Southwest University, Chongqing, China; Deng, Y., School of Computer and Information Science, Southwest University, Chongqing, China, Department of Civil and Environmental Engineering, School of Engineering, Vanderbilt University, Nashville, TN, United States","We propose an efficient bio-inspired algorithm for design of optimal supply chain networks in a competitive oligopoly markets. The firms compete in manufacture, storage and distribution of a product to several markets. Each firm aims at maximisation of its own profit by optimising the design capacity and product flow in the supply chain. We model the supply chain network as a multi-layer graph of manufacturing nodes, distribution nodes and storage centres. To optimise the network, we adopt the mechanisms of a foraging behaviour of slime mould Physarum polycephalym. First, we extend the original Physarum model to deal with networks with multiple sources and sinks. Second, we develop a novel method to solve the user equilibrium (UE) problem by exploiting the adaptivity of the Physarum model: we update the link costs according to the product flow. Third, we refer to an equivalent transformation between system optimum problem and UE problem to determine the optimal product flows and design capacities of a supply chain. At last, we present an approach to update the amount of product supplied by each firm. By comparing our solutions with that in Nagurney (2010b) on several numerical examples, we demonstrate the efficiency and practicality of the proposed method. © 2016 Informa UK Limited, trading as Taylor & Francis Group.","artificial intelligence; decision support systems; network oligopolies; Physarum; supply chain design","Algorithms; Artificial intelligence; Commerce; Competition; Decision support systems; Design; Manufacture; Numerical methods; Profitability; Supply chains; Bio-inspired algorithms; Equivalent transformations; Oligopolistic competition; Optimal supply chain; Physarum; Supply chain design; Supply chain network; Supply chain network design; Product design","SWU110021; National Natural Science Foundation of China, NSFC: 61174022, 71471158; Hong Kong Polytechnic University, PolyU; Natural Science Foundation of Chongqing: 2010BA2003; National High-tech Research and Development Program: 2013AA013801","The work is partially supported by Chongqing Natural Science Foundation [grant number CSCT, 2010BA2003]; National Natural Science Foundation of China [grant number 61174022], [grant number 71471158]; National High Technology Research and Development Program of China (863 Program) [grant number 2013AA013801]; Doctor Funding of Southwest University [grant number SWU110021]. The authors also would like to thank The Hong Kong Polytechnic University Research Committee for financial and technical support.",,,,,,,,,,"Deng, Y.; School of Computer and Information Science, China; email: ydeng@swu.edu.cn",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-84979017491
"Kumar V.N.S.A., Kumar V., Brady M., Garza-Reyes J.A., Simpson M.","57202530548;36835614000;16068313100;35310169200;12238884200;","Resolving forward-reverse logistics multi-period model using evolutionary algorithms",2017,"International Journal of Production Economics","183",,,"458","469",,39,"10.1016/j.ijpe.2016.04.026","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975499953&doi=10.1016%2fj.ijpe.2016.04.026&partnerID=40&md5=6395ccd7ada524a0b411f2c2e0ab3ae5","Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur, India; Bristol Business School, University of the West of England, Bristol, United Kingdom; Department of Management, Dublin City University Business School, Dublin, Ireland; Derby Business School, The University of Derby, Derby, United Kingdom; Sheffield University Management School, University of the Sheffield, Sheffield, United Kingdom","Kumar, V.N.S.A., Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur, India; Kumar, V., Bristol Business School, University of the West of England, Bristol, United Kingdom; Brady, M., Department of Management, Dublin City University Business School, Dublin, Ireland; Garza-Reyes, J.A., Derby Business School, The University of Derby, Derby, United Kingdom, Sheffield University Management School, University of the Sheffield, Sheffield, United Kingdom; Simpson, M., Sheffield University Management School, University of the Sheffield, Sheffield, United Kingdom","In the changing competitive landscape and with growing environmental awareness, reverse logistics issues have become prominent in manufacturing organizations. As a result there is an increasing focus on green aspects of the supply chain to reduce environmental impacts and ensure environmental efficiency. This is largely driven by changes made in government rules and regulations with which organizations must comply in order to successfully operate in different regions of the world. Therefore, manufacturing organizations are striving hard to implement environmentally efficient supply chains while simultaneously maximizing their profit to compete in the market. To address the issue, this research studies a forward-reverse logistics model. This paper puts forward a model of a multi-period, multi-echelon, vehicle routing, forward-reverse logistics system. The network considered in the model assumes a fixed number of suppliers, facilities, distributors, customer zones, disassembly locations, re-distributors and second customer zones. The demand levels at customer zones are assumed to be deterministic. The objective of the paper is to maximize the total expected profit and also to obtain an efficient route for the vehicle corresponding to an optimal/near optimal solution. The proposed model is resolved using Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) algorithms. The findings show that for the considered model, AIS works better than the PSO. This information is important for a manufacturing organization engaged in reverse logistics programs and in running units efficiently. This paper also contributes to the limited literature on reverse logistics that considers costs and profit as well as vehicle route management. © 2016 Elsevier Ltd","AIS; Cost; Profit; PSO; Reverse logistics; Supply chain; Vehicle routing","Algorithms; Artificial intelligence; Costs; Crashworthiness; Environmental impact; Evolutionary algorithms; Manufacture; Optimization; Particle swarm optimization (PSO); Profitability; Sales; Societies and institutions; Supply chains; Vehicle routing; Vehicles; Artificial Immune System; Environmental awareness; Environmental efficiency; Manufacturing organizations; Optimal solutions; Particle swarm optimization algorithm; Reverse logistics; Rules and regulations; Logistics",,,,,,,,,,,,"Kumar, V.; Bristol Business School, United Kingdom; email: Vikas.Kumar@uwe.ac.uk",,"Elsevier B.V.",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","All Open Access, Green",Scopus,2-s2.0-84975499953
"Zhang S., Lee C.K.M., Wu K., Choy K.L.","56244760100;24468281700;56816074600;7005477047;","Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels",2016,"Expert Systems with Applications","65",,,"87","99",,67,"10.1016/j.eswa.2016.08.037","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982131483&doi=10.1016%2fj.eswa.2016.08.037&partnerID=40&md5=5e5781226d1963f010020aeae6c2c0aa","Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong; School of Mechanical Aerospace Engineering, Nanyang Technological University, Singapore","Zhang, S., Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong; Lee, C.K.M., Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong; Wu, K., School of Mechanical Aerospace Engineering, Nanyang Technological University, Singapore; Choy, K.L., Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong","The emergence of Omni-channel has affected the practical design of the supply chain network (SCN) with the purpose of providing better products and services for customers. In contrast to the conventional SCN, a new strategic model for designing SCN with multiple distribution channels (MDCSCN) is introduced in this research. The MDCSCN model benefits customers by providing direct products and services from available facilities instead of the conventional flow of products and services. Sustainable objectives, i.e., reducing economic cost, enlarging customer coverage and weakening environmental influences, are involved in designing the MDCSN. A modified multi-objective artificial bee colony (MOABC) algorithm is introduced to solve the MDCSCN model, which integrates the priority-based encoding mechanism, the Pareto optimality and the swarm intelligence of the bee colony. The effect of the MDCSCN model are examined and validated through numerical experiment. The MDCSCN model is innovative and pioneering as it meets the latest requirements and outperforms the conventional SCN. More importantly, it builds the foundation for an intelligent customer order assignment system. The effectiveness and efficiency of the MOABC algorithm is evaluated in comparison with the other popular multi-objective meta-heuristic algorithm with promising results. © 2016 Elsevier Ltd","Artificial bee colony; Multi-objective optimization; Multiple distribution channels; Supply chain network; Swarm intelligence","Artificial intelligence; Heuristic algorithms; Optimization; Pareto principle; Product design; Sales; Supply chains; Artificial bee colonies; Effectiveness and efficiencies; Environmental influences; Multi-objective artificial bee colonies; Multiple distribution; Supply chain network; Sustainable supply chains; Swarm Intelligence; Multiobjective optimization","Hong Kong Polytechnic University, PolyU","This work was supported by the Hong Kong Polytechnic University. Our gratitude is also extended to the Research Committee and the Department of Industrial and Systems Engineering of the Hong Kong Polytechnic University for support in this project (#4-RTY0) (H-ZDAK) and (G-YBJE).",,,,,,,,,,"Lee, C.K.M.; Department of Industrial and Systems Engineering, China; email: ckm.lee@polyu.edu.hk",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84982131483
"Moncayo-Martínez L.A., Mastrocinque E.","36872449700;54784848400;","A multi-objective intelligent water drop algorithm to minimise cost Of goods sold and time to market in logistics networks",2016,"Expert Systems with Applications","64",,,"455","466",,15,"10.1016/j.eswa.2016.08.003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984829451&doi=10.1016%2fj.eswa.2016.08.003&partnerID=40&md5=58401ee82cc9e059b04deb8e7dd3392f","Department of Industrial Engineering and Operations, Instituto Tecnológico Autónomo de México (ITAM), Rio Hondo #1, Col. Progreso Tizapan, C.P. 01080, Mexico City, Mexico; School of Management, Royal Holloway, University of London, Egham Hill, TW20 0EX, Egham, United Kingdom","Moncayo-Martínez, L.A., Department of Industrial Engineering and Operations, Instituto Tecnológico Autónomo de México (ITAM), Rio Hondo #1, Col. Progreso Tizapan, C.P. 01080, Mexico City, Mexico; Mastrocinque, E., School of Management, Royal Holloway, University of London, Egham Hill, TW20 0EX, Egham, United Kingdom","The Intelligent Water Drop (IWD) algorithm is inspired by the movement of natural water drops (WD) in a river. A stream can find an optimum path considering the conditions of its surroundings to reach its ultimate goal, which is often a sea. In the process of reaching such destination, the WD and the environment interact with each other as the WD moves through the river bed. Similarly, the supply chain problem can be modelled as a flow of stages that must be completed and optimised to obtain a finished product that is delivered to the end user. Every stage may have one or more options to be satisfied such as suppliers, manufacturing or delivery options. Each option is characterised by its time and cost. Within this context, multi–objective optimisation approaches are particularly well suited to provide optimal solutions. This problem has been classified as NP hard; thus, this paper proposes an approach aiming to solve the logistics network problem using a modified multi–objective extension of the IWD which returns a Pareto set. Artificial WD, flowing through the supply chain, will simultaneously minimise the cost of goods sold and the lead time of every product involved by using the concept of Pareto optimality. The proposed approach has been tested over instances widely used in literature yielding promising results which are supported by the performance measurements taken by comparison to the ant colony meta-heuristic as well as the true fronts obtained by exhaustive enumeration. The Pareto set returned by IWD is computed in 4 s and the generational distance, spacing, and hyper–area metrics are very close to those computed by exhaustive enumeration. Therefore, our main contribution is the design of a new algorithm that overcomes the algorithm proposed by Moncayo-Martínez and Zhang (2011). This paper contributes to enhance the current body of knowledge of expert and intelligent systems by providing a new, effective and efficient IWD-based optimisation method for the design and configuration of supply chain and logistics networks taking into account multiple objectives simultaneously. © 2016 Elsevier Ltd","Bill of materials; Bi–objective optimisation; Logistics networks; Pareto optimality; Swarm intelligence; Water drop intelligence","Algorithms; Ant colony optimization; Artificial intelligence; Costs; Drops; Enterprise resource planning; Intelligent systems; Logistics; Pareto principle; Rivers; Supply chains; Transportation; Bill of materials; Logistics network; Objective optimisation; Pareto-optimality; Swarm Intelligence; Water drop; Optimization","Consejo Nacional de Ciencia y Tecnología, CONACYT","The completion of this article was supported by the Asociación Mexicana de Cultura A.C. and the Mexico’s National Council of Science and Technology (CONACyT).",,,,,,,,,,"Moncayo-Martínez, L.A.; Department of Industrial Engineering and Operations, Rio Hondo #1, Col. Progreso Tizapan, C.P. 01080, Mexico City, Mexico; email: luis.moncayo@itam.mx",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","All Open Access, Green",Scopus,2-s2.0-84984829451
"Dev N.K., Shankar R., Gunasekaran A., Thakur L.S.","26325297200;10638891800;56238759300;7003859504;","A hybrid adaptive decision system for supply chain reconfiguration",2016,"International Journal of Production Research","54","23",,"7100","7114",,20,"10.1080/00207543.2015.1134842","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954287667&doi=10.1080%2f00207543.2015.1134842&partnerID=40&md5=d84de002ef63fee61f33251168d2b44c","Department of Mechanical Engineering, Dayalbagh Educational Institute, Agra, India; Department of Management Studies, Indian Institute of Technology Delhi, New Delhi, India; Department of Decision and Information Sciences, University of Massachusetts, Dartmouth, MA, United States; Department of Operations and Information Management, University of Connecticut, Storrs, CT, United States","Dev, N.K., Department of Mechanical Engineering, Dayalbagh Educational Institute, Agra, India; Shankar, R., Department of Management Studies, Indian Institute of Technology Delhi, New Delhi, India; Gunasekaran, A., Department of Decision and Information Sciences, University of Massachusetts, Dartmouth, MA, United States; Thakur, L.S., Department of Operations and Information Management, University of Connecticut, Storrs, CT, United States","Due to short product life cycle, it is expedient to reconfiguration an existing supply chain from time to time. Companies need to impose the standards on operational units for finding the best or the near best alternative configuration. Thus, it becomes imperative to effectively adapt various enablers in a supply chain by understanding the dynamics between them that help to reconfigure a supply chain for high levels of performance. This paper presents an integration of agent-based simulation and decision tree learning as the data mining techniques to determine adaptive decisions of operational units of a mobile phone supply chain. Agent-based simulation output is subjected to data mining analysis to understand system behaviour in terms of interactions and the factors influencing the performance. An entropy-based formulation is proposed as the basis for comparing different operational units in the supply chain. The insights obtained are then encapsulated as operational rules and guidelines supporting better decision-making. © 2016 Informa UK Limited, trading as Taylor & Francis Group.","agent-based discrete event simulation; data mining; decision support system; entropy; supply chain management","Artificial intelligence; Decision making; Decision support systems; Decision trees; Discrete event simulation; E-learning; Entropy; Information management; Life cycle; Reconfigurable hardware; Supply chain management; Trees (mathematics); Adaptive decision systems; Agent based; Agent based simulation; Alternative configurations; Decision tree learning; Entropy-based; Operational units; Short product; Data mining",,,,,,,,,,,,"Dev, N.K.; Department of Mechanical Engineering, India; email: navinkumardev@yahoo.com",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-84954287667
"Dweiri F., Kumar S., Khan S.A., Jain V.","6508032260;55616292300;55637292800;35749011500;","Designing an integrated AHP based decision support system for supplier selection in automotive industry",2016,"Expert Systems with Applications","62",,,"273","283",,235,"10.1016/j.eswa.2016.06.030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976543444&doi=10.1016%2fj.eswa.2016.06.030&partnerID=40&md5=1cafce50dc74215090bed179fb4b6cdd","Industrial Engineering and Engineering Management Department, College of Engineering, University of Sharjah, P. O. Box 27272, Sharjah, United Arab Emirates; Operations and Supply Chain Management Department, Opus College of Business, University of St. Thomas, 1000 LaSalle AvenueMN, Minneapolis, 55403, United States; Victoria Business School, Victoria University of Wellington, 23, Lambton Quay, Pipitea Campus, Wellington, 6140, New Zealand","Dweiri, F., Industrial Engineering and Engineering Management Department, College of Engineering, University of Sharjah, P. O. Box 27272, Sharjah, United Arab Emirates; Kumar, S., Operations and Supply Chain Management Department, Opus College of Business, University of St. Thomas, 1000 LaSalle AvenueMN, Minneapolis, 55403, United States; Khan, S.A., Industrial Engineering and Engineering Management Department, College of Engineering, University of Sharjah, P. O. Box 27272, Sharjah, United Arab Emirates; Jain, V., Victoria Business School, Victoria University of Wellington, 23, Lambton Quay, Pipitea Campus, Wellington, 6140, New Zealand","Purpose The purpose of this paper is to propose a decision support model for supplier selection based on analytic hierarchy process (AHP) using a case of automotive industry in a developing country of Pakistan and further performs sensitivity analysis to check the robustness of the supplier selection decision. Methodology The model starts by identifying the main criteria (price, quality, delivery and service) using literature review and ranking the main criteria based on experts’ opinions using AHP. The second stage in the adopted methodology is the identification of sub criteria and ranking them on the basis of main criteria. Lastly perform sensitivity analysis to check the robustness of the decision using Expert Choiceۛ software. Findings The suppliers are selected and ranked based on sub criteria. Sensitivity analysis suggests the effects of changes in the main criteria on the suppliers ranking. The use of AHP in the supplier selection gives the decision maker the confidence of the consistency and the robustness throughout the process. Practical implications The AHP methodology adopted in this study provides managers in automotive industry in Pakistan with the insights of the various factors that need to be considered while selecting suppliers for their organizations. The selected approach also aids them in prioritizing the criterion. Managers can utilize the hierarchical structure of adopted supplier selection methodology suggested in this study to rank the suppliers on the basis of various factors/criteria. Originality/value This study makes three novel contributions in supplier selection area. First, AHP is applied to automotive industry and use of AHP in the supplier selection gives decision maker the confidence of the consistency. Second, sensitivity analysis enables in understanding the effects of changes in the main criteria on the suppliers ranking and help decision maker to check the robustness throughout the process. Last, we find it important to come with a simple methodology for managers of automotive industry so that they can select the best suppliers. Moreover, this approach will also help managers in dividing the complex decision making problem into simpler hierarchy. © 2016 Elsevier Ltd","Analytic hierarchy process (AHP); Decision support system; Multi-criteria decision making (MCDM); Sensitivity analysis; Supplier selection; Supply chain management","Analytic hierarchy process; Artificial intelligence; Automotive industry; Decision support systems; Developing countries; Hierarchical systems; Managers; Sensitivity analysis; Supply chain management; Analytic hierarchy process (ahp); Complex decision; Decision support models; Hierarchical structures; Literature reviews; Multi-criteria decision making; Quality , Delivery; Supplier selection; Decision making",,,,,,,,,,,,"Kumar, S.; Operations and Supply Chain Management Department, 1000 LaSalle AvenueMN, United States; email: skumar@stthomas.edu",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84976543444
"Mogre R., Talluri S.S., Damico F.","27868006500;7003714927;57190977939;","A decision framework to mitigate supply chain risks: An application in the offshore-wind industry",2016,"IEEE Transactions on Engineering Management","63","3","7492193","316","325",,25,"10.1109/TEM.2016.2567539","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974853325&doi=10.1109%2fTEM.2016.2567539&partnerID=40&md5=998e5b4fa9f5b83d7c5891461ae9e714","Durham University Business School, Durham University, Durham, DH1 3LB, United Kingdom; Eli Broad College of Business, Michigan State University, East Lansing, MI  48825, United States; EDF Energy R and D UK Centre, London, SW1E 5JL, United Kingdom","Mogre, R., Durham University Business School, Durham University, Durham, DH1 3LB, United Kingdom; Talluri, S.S., Eli Broad College of Business, Michigan State University, East Lansing, MI  48825, United States; Damico, F., EDF Energy R and D UK Centre, London, SW1E 5JL, United Kingdom","Decision support systems (DSSs) for supply chain risk management benefit from a holistic approach for mitigating risks, which include identification and assessment of risks and evaluation and selection of measures to appease risks. However, previous studies in this area overlooked probability estimation, measure selection, and assessment of interdependence of risks and measures. We aim to fill these gaps in the literature by proposing a two-stage DSSs that will assist managers in not only select mitigation strategies for supply chain risks, but also mitigation tactics when risks occur. Our DSS employs a novel matrix formulation for decision-tree analysis, which integrates expert judgments. We applied our models to the supply chain of a fast-expanding offshore-wind industry, which faces high levels of exposure to risks because of the associated complexities in this domain. The results demonstrate how to select mitigation strategies and mitigation tactics for managing supply chain risks within the offshore-wind industry. © 1988-2012 IEEE.","Decision framework; decision trees; offshore-wind industry; risk mitigation; supply chain risk","Artificial intelligence; Decision support systems; Decision trees; Risk assessment; Risk management; Risk perception; Supply chains; Decision framework; Decision support system (DSSs); Decision tree analysis; Holistic approach; Matrix formulation; Mitigation strategy; Probability estimation; Supply chain risk management; Risks",,,,,,,,,,,,"Mogre, R.; Durham University Business School, United Kingdom; email: riccardo.mogre@durham.ac.uk",,"Institute of Electrical and Electronics Engineers Inc.",00189391,,IEEMA,,"English","IEEE Trans Eng Manage",Article,"Final","All Open Access, Green",Scopus,2-s2.0-84974853325
"Nikolopoulos K.I., Babai M.Z., Bozos K.","57530895400;23974512700;35191643600;","Forecasting supply chain sporadic demand with nearest neighbor approaches",2016,"International Journal of Production Economics","177",,,"139","148",,29,"10.1016/j.ijpe.2016.04.013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84968820839&doi=10.1016%2fj.ijpe.2016.04.013&partnerID=40&md5=5e487cb5dbdd7e28f01aa0c2263f3033","ForLAB, Bangor Business School, Prifysgol Bangor University, Bangor, Gwynedd, United Kingdom; Kedge Business School, Bordeaux, France; Leeds University Business School, University of Leeds, Leeds, United Kingdom","Nikolopoulos, K.I., ForLAB, Bangor Business School, Prifysgol Bangor University, Bangor, Gwynedd, United Kingdom; Babai, M.Z., Kedge Business School, Bordeaux, France; Bozos, K., Leeds University Business School, University of Leeds, Leeds, United Kingdom","One of the biggest challenges in Supply Chain Management (SCM) is to forecast sporadic demand. Our forecasting methods' arsenal includes Croston's method, SBA and TSB as well as some more recent non-parametric advances, but none of these can identify and extrapolate patterns existing in data; this is essential as these patterns do appear quite often, driven by infrequent but nevertheless repetitive managerial practices. One could claim such patterns could be picked up by Artificial Intelligence approaches, however these do need large training datasets, unfortunately non-existent in industrial time series. Nearest neighbors (NN) can however operate in these latter contexts, and pick up patterns even in short series. In this research we propose applying NN for supply chain data and we investigate the conditions under which these perform adequately through an extensive simulation. Furthermore, via an empirical investigation in automotive data we provide evidence that practitioners could benefit from employing supervised NN approaches. The contribution of this research is not in the development of a new theory, but in the proposition of a new conceptual framework that brings existing theory (i.e. NN) from Computer Science and Statistics and applies it successfully in an SCM setting. © 2016 Elsevier B.V. All rights reserved.","Demand forecasting; Exponential smoothing; Logistics; Nearest neighbors; Supply chain","Artificial intelligence; Logistics; Supply chain management; Supply chains; Conceptual frameworks; Demand forecasting; Empirical investigation; Exponential smoothing; Extensive simulations; Nearest neighbors; Nearest-neighbor approaches; Supply chain managements (SCM); Forecasting",,,,,,,,,,,,"Nikolopoulos, K.I.; ForLAB, United Kingdom; email: k.nikolopoulos@bangor.ac.uk",,"Elsevier B.V.",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","All Open Access, Green",Scopus,2-s2.0-84968820839
"Syntetos A.A., Babai Z., Boylan J.E., Kolassa S., Nikolopoulos K.","8320316800;57110353400;7006873512;15029816500;57530895400;","Supply chain forecasting: Theory, practice, their gap and the future",2016,"European Journal of Operational Research","252","1",,"1","26",,132,"10.1016/j.ejor.2015.11.010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960200121&doi=10.1016%2fj.ejor.2015.11.010&partnerID=40&md5=26679c8c0beaef953ed2bb22b82f88fd","Logistics and Operations Management, Cardiff Business School, Cardiff University, Cardiff, CF10 3EU, United Kingdom; Operations and Supply Chain Management, Kedge Business School, 680 cours de la Libfzration, Talence Cedex, 33405, France; Department of Management Science, Lancaster University Management School, Lancaster University, Bailrigg, Lancaster, LA1 4YX, United Kingdom; Products and Innovation Suite Engineering Consumer Industries (PI SE CI), SAP (Switzerland) AG, Bahnstrasse 1, Tgerwilen, 8274, Switzerland; Bangor Business School, Bangor University, College Road, Bangor, LL57 2DG, United Kingdom","Syntetos, A.A., Logistics and Operations Management, Cardiff Business School, Cardiff University, Cardiff, CF10 3EU, United Kingdom; Babai, Z., Operations and Supply Chain Management, Kedge Business School, 680 cours de la Libfzration, Talence Cedex, 33405, France; Boylan, J.E., Department of Management Science, Lancaster University Management School, Lancaster University, Bailrigg, Lancaster, LA1 4YX, United Kingdom; Kolassa, S., Products and Innovation Suite Engineering Consumer Industries (PI SE CI), SAP (Switzerland) AG, Bahnstrasse 1, Tgerwilen, 8274, Switzerland; Nikolopoulos, K., Bangor Business School, Bangor University, College Road, Bangor, LL57 2DG, United Kingdom","Supply Chain Forecasting (SCF) goes beyond the operational task of extrapolating demand requirements at one echelon. It involves complex issues such as supply chain coordination and sharing of information between multiple stakeholders. Academic research in SCF has tended to neglect some issues that are important in practice. In areas of practical relevance, sound theoretical developments have rarely been translated into operational solutions or integrated in state-of-the-art decision support systems. Furthermore, many experience-driven heuristics are increasingly used in everyday business practices. These heuristics are not supported by substantive scientific evidence; however, they are sometimes very hard to outperform. This can be attributed to the robustness of these simple and practical solutions such as aggregation approaches for example (across time, customers and products). This paper provides a comprehensive review of the literature and aims at bridging the gap between theory and practice in the existing knowledge base in SCF. We highlight the most promising approaches and suggest their integration in forecasting support systems. We discuss the current challenges both from a research and practitioner perspective and provide a research and application agenda for further work in this area. Finally, we make a contribution in the methodology underlying the preparation of review articles by means of involving the forecasting community in the process of deciding both the content and structure of this paper. © 2015 Elsevier B.V. All rights reserved.","Forecasting empirical research; Forecasting software; Literature review; Supply chain forecasting","Artificial intelligence; Coordination reactions; Decision support systems; Knowledge based systems; Query languages; Supply chains; Empirical research; Forecasting software; Forecasting support system; Literature reviews; Operational solutions; Research and application; Supply chain coordination; Theoretical development; Forecasting",,,,,,,,,,,,"Syntetos, A.A.; Logistics and Operations Management, United Kingdom; email: SyntetosA@cardiff.ac.uk",,"Elsevier",03772217,,EJORD,,"English","Eur J Oper Res",Review,"Final","All Open Access, Green",Scopus,2-s2.0-84960200121
"Ponte B., Costas J., Puche J., De La Fuente D., Pino R.","56029133100;57062332100;45561439400;7004113754;7006690508;","Holism versus reductionism in supply chain management: An economic analysis",2016,"Decision Support Systems","86",,,"83","94",,18,"10.1016/j.dss.2016.03.010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979723558&doi=10.1016%2fj.dss.2016.03.010&partnerID=40&md5=30bac2ee606b94e27a979ce4860df18e","Department of Business Administration, University of Oviedo, Gijón, Spain; Polytechnic Institute of Viana Do Castelo, Valença, Portugal; Department of Applied Economics, University of Burgos, Burgos, Spain","Ponte, B., Department of Business Administration, University of Oviedo, Gijón, Spain; Costas, J., Polytechnic Institute of Viana Do Castelo, Valença, Portugal; Puche, J., Department of Applied Economics, University of Burgos, Burgos, Spain; De La Fuente, D., Department of Business Administration, University of Oviedo, Gijón, Spain; Pino, R., Department of Business Administration, University of Oviedo, Gijón, Spain","Since supply chains are increasingly built on complex interdependences, concerns to adopt new managerial approaches based on collaboration have surged. Nonetheless, implementing an efficient collaborative solution is a wide process where several obstacles must be faced. This work explores the key role of experimentation as a model-driven decision support system for managers in the convoluted decision-making process required to evolve from a reductionist approach (where the overall strategy is the sum of individual strategies) to a holistic approach (where global optimization is sought through collaboration). We simulate a four-echelon supply chain within a large noise scenario, while a fractional factorial design of experiments (DoE) with eleven factors was used to explore cause-effect relationships. By providing evidence in a wide range of conditions of the superiority of the holistic approach, supply chain participants can be certain to move away from their natural reductionist behavior. Thereupon, practitioners focus on implementing the solution. The theory of constraints (TOC) defines an appropriate framework, where the Drum-Buffer-Rope (DBR) method integrates supply chain processes and synchronizes decisions. In addition, this work provides evidence of the need for aligning incentives in order to eliminate the risk to deviate. Modeling and simulation, especially agent-based techniques, allows practitioners to develop awareness of complex organizational problems. Hence, these prototypes can be interpreted as forceful laboratories for decision making and business transformation. © 2016 Elsevier B.V. All rights reserved.","Drum-Buffer-Rope; Model-driven decision support systems; OUT policy; Theory of constraints; Throughput accounting","Artificial intelligence; Decision making; Decision theory; Design of experiments; Economic analysis; Global optimization; Managers; Rope; Supply chain management; Business transformations; Cause-effect relationships; Drum-buffer-rope; Fractional factorial designs; Model-driven; Organizational problems; Theory of constraint; Throughput accounting; Decision support systems",,,,,,,,,,,,"Ponte, B.; Department of Business Administration, Spain; email: ponteborja@uniovi.es",,"Elsevier B.V.",01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","All Open Access, Green",Scopus,2-s2.0-84979723558
"Zimmer K., Fröhling M., Schultmann F.","56921538300;23766956500;6602564223;","Sustainable supplier management - A review of models supporting sustainable supplier selection, monitoring and development",2016,"International Journal of Production Research","54","5",,"1412","1442",,222,"10.1080/00207543.2015.1079340","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945242270&doi=10.1080%2f00207543.2015.1079340&partnerID=40&md5=fb7f3b27f9a02703b3061caa3ca17c46","Institute of Industrial Production (IIP), Department of Economics and Management, Karlsruhe Institute of Technology (KIT), Germany; Entrepreneurship, Commercialisation and Innovation Centre (ECIC), University of Adelaide, Adelaide, Australia","Zimmer, K., Institute of Industrial Production (IIP), Department of Economics and Management, Karlsruhe Institute of Technology (KIT), Germany; Fröhling, M., Institute of Industrial Production (IIP), Department of Economics and Management, Karlsruhe Institute of Technology (KIT), Germany; Schultmann, F., Institute of Industrial Production (IIP), Department of Economics and Management, Karlsruhe Institute of Technology (KIT), Germany, Entrepreneurship, Commercialisation and Innovation Centre (ECIC), University of Adelaide, Adelaide, Australia","In the last two decades, pressure from various stakeholders has forced many companies to establish environmental and social improvements both in their company and their supply chains. The growing number of journal publications and conference proceedings confirms this change also in academia. The aim of this paper is to analyse and review scientific literature on sustainable supplier management (SSM) with a focus on formal models supporting decision-making in sustainable supplier selection, monitoring and development. For this purpose, a framework on SSM is proposed and a comprehensive content analysis including a criteria analysis is carried out. Beyond this, in total 143 peer-reviewed publications between 1997 and 2014 have been analysed to identify both established and overlooked research fields. Major findings are the rapidly growing interest of this topic in academia in recent years, the predominance of Analytic Hierarchy Process, Analytic Network Process and fuzzy-based approaches, the focus on the final evaluation and selection process step and the rare investigation of social and quantitative metrics. This review may be useful for practitioners and scientists as it outlines major characteristics in this field, which can serve as a basis for further research. © 2015 Taylor & Francis.","decision support systems; evaluation criteria; literature review; purchasing; supplier selection; sustainability","Analytic hierarchy process; Artificial intelligence; Decision support systems; Purchasing; Supply chains; Sustainable development; Analytic network process; Evaluation criteria; Literature reviews; Quantitative metrics; Scientific literature; Supplier management; Supplier selection; Sustainable supplier selections; Decision making",,,,,,,,,,,,"Zimmer, K.; Institute of Industrial Production (IIP), Germany; email: konrad.zimmer@kit.edu",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-84945242270
"Kumar V., Holt D., Ghobadian A., Garza-Reyes J.A.","36835614000;55316234600;6603717481;35310169200;","Developing green supply chain management taxonomy-based decision support system",2015,"International Journal of Production Research","53","21",,"6372","6389",,39,"10.1080/00207543.2014.917215","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941942157&doi=10.1080%2f00207543.2014.917215&partnerID=40&md5=f17640b7c687185a49db7f874422f7ec","Bristol Business School, University of the West of England, Bristol, United Kingdom; Essex Business School, University of Essex, Essex, United Kingdom; Henley Business School, University of Reading, Reading, United Kingdom; Centre for Supply Chain Improvement, University of Derby, Derby, United Kingdom","Kumar, V., Bristol Business School, University of the West of England, Bristol, United Kingdom; Holt, D., Essex Business School, University of Essex, Essex, United Kingdom; Ghobadian, A., Henley Business School, University of Reading, Reading, United Kingdom; Garza-Reyes, J.A., Centre for Supply Chain Improvement, University of Derby, Derby, United Kingdom","The aim of this paper is to develop a comprehensive taxonomy of green supply chain management (GSCM) practices and develop a structural equation modelling-driven decision support system following GSCM taxonomy for managers to provide better understanding of the complex relationship between the external and internal factors and GSCM operational practices. Typology and/or taxonomy play a key role in the development of social science theories. The current taxonomies focus on a single or limited component of the supply chain. Furthermore, they have not been tested using different sample compositions and contexts, yet replication is a prerequisite for developing robust concepts and theories. In this paper, we empirically replicate one such taxonomy extending the original study by (a) developing broad (containing the key components of supply chain) taxonomy; (b) broadening the sample by including a wider range of sectors and organisational size; and (c) broadening the geographic scope of the previous studies. Moreover, we include both objective measures and subjective attitudinal measurements. We use a robust two-stage cluster analysis to develop our GSCM taxonomy. The main finding validates the taxonomy previously proposed and identifies size, attitude and level of environmental risk and impact as key mediators between internal drivers, external drivers and GSCM operational practices. © 2014 Taylor & Francis.","decision support; environmental attitude; green supply chain management; structural equation modelling; taxonomy","Artificial intelligence; Cluster analysis; Decision making; Decision support systems; Taxonomies; Complex relationships; Decision supports; Environmental attitudes; Green supply chain management; Operational practices; Social science theory; Structural equation modelling; Two-stage cluster analysis; Supply chain management",,,,,,,,,,,,"Kumar, V.; Bristol Business School, United Kingdom",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-84941942157
"Borade A.B., Sweeney E.","36612850300;16508035100;","Decision support system for vendor managed inventory supply chain: A case study",2015,"International Journal of Production Research","53","16",,"4789","4818",,23,"10.1080/00207543.2014.993047","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84932196902&doi=10.1080%2f00207543.2014.993047&partnerID=40&md5=ac82e890397dfd2cae03056c37bf9c92","Mechanical Engineering Department, Jawaharlal Darda Institute of Engineering and Technology, Yavatmal, India; Engineering Systems and Management, School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom","Borade, A.B., Mechanical Engineering Department, Jawaharlal Darda Institute of Engineering and Technology, Yavatmal, India; Sweeney, E., Engineering Systems and Management, School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom","Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers manages the inventory of retailers and takes responsibility for making decisions related to the timing and extent of inventory replenishment. VMI partnerships help organisations to reduce demand variability, inventory holding and distribution costs. This study provides empirical evidence that significant economic benefits can be achieved with the use of a genetic algorithm (GA)-based decision support system (DSS) in a VMI supply chain. A two-stage serial supply chain in which retailers and their supplier are operating VMI in an uncertain demand environment is studied. Performance was measured in terms of cost, profit, stockouts and service levels. The results generated from GA-based model were compared to traditional alternatives. The study found that the GA-based approach outperformed traditional methods and its use can be economically justified in small- and medium-sized enterprises (SMEs). © 2014 Taylor & Francis.","case study; decision support system; genetic algorithm; vendor-managed inventory","Artificial intelligence; Chains; Decision making; Economic analysis; Genetic algorithms; Inventory control; Supply chains; Decision support system (dss); Demand variability; Distribution costs; Inventory holding; Inventory management; Inventory replenishment; Small and medium-sized enterprise; Vendor managed Inventory; Decision support systems",,,,,,,,,,,,"Borade, A.B.; Mechanical Engineering Department, India",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-84932196902
"Peiris K.D.A., Jung J., Gallupe R.B.","55308507100;56673267400;6601965822;","Building and evaluating ESET: A tool for assessing the support given by an enterprise system to supply chain management",2015,"Decision Support Systems","77",,,"41","54",,14,"10.1016/j.dss.2015.05.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930619670&doi=10.1016%2fj.dss.2015.05.004&partnerID=40&md5=d2942f81bc5a7cdbc6c8104fd24f6988","Department of Information Systems, Business School, University of Auckland, New Zealand; School of Business, Queen's University, Kingston, Ontario, Canada","Peiris, K.D.A., Department of Information Systems, Business School, University of Auckland, New Zealand; Jung, J., Department of Information Systems, Business School, University of Auckland, New Zealand; Gallupe, R.B., School of Business, Queen's University, Kingston, Ontario, Canada","Modern organisations must effectively manage their supply chains, to exist and grow. Supply chains draw information extensively from enterprise systems (ESs) of participating businesses. Despite that supply chains frequently depend on information from ES to succeed, not much research on measuring the effectiveness of information transfers between these systems has been published. This paper describes the building and evaluation of a flexible decision support tool that evaluates the impact an ES has on supply chain management (SCM), thereby filling a gap in the SCM assessment portfolio of tools. The main purpose of the Enterprise System Evaluating Tool (ESET), is to measure the support given by ES to SCM and identify process points at which such support fails. Thus ESET empowers organisations with knowledge to improve their supply chain performance by modifying and/or enhancing the ES. A case study based approach was used to evaluate ESET to ascertain its utility by applying it in two Fortune 100 organisations within one industry. In future research, ESET will be applied across many industries, to quantitatively evaluate ESET and refine it further. Analytics on data gathered from these organisations may then enlighten researchers and practitioners on the current state of support given by ES to SCM. © 2015 Elsevier B.V. All rights reserved.","Enterprise Systems; Impact of Enterprise Systems on Supply Chain Management; Qualitative Evaluation of Decision Support Systems; Supply Chain Management Systems; Systems Evaluation","Artificial intelligence; Decision making; Decision support systems; Enterprise resource planning; Societies and institutions; Decision support tools; Enterprise system; Information transfers; Qualitative evaluations; Supply chain management system; Supply chain managements (SCM); Supply chain performance; Systems evaluations; Supply chain management",,,,,,,,,,,,"Peiris, K.D.A.; Department of Information Systems, Business School, University of AucklandNew Zealand",,"Elsevier",01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-84930619670
"Court C.D., Munday M., Roberts A., Turner K.","55446563700;7005612035;7404499050;9334515000;","Can hazardous waste supply chain 'hotspots' be identified using an input-output framework?",2015,"European Journal of Operational Research","241","1",,"177","187",,20,"10.1016/j.ejor.2014.08.011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028095419&doi=10.1016%2fj.ejor.2014.08.011&partnerID=40&md5=a13d79000d2f82a0ae0b353425fa1b96","MRIGlobal and Regional Research Institute, West Virginia University, 425 Volker Boulevard, Kansas City, MO  64110, United States; Cardiff Business School, Cardiff University, Colum Drive, Cardiff, CF10 3EU, United Kingdom; School of Management and Languages, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom","Court, C.D., MRIGlobal and Regional Research Institute, West Virginia University, 425 Volker Boulevard, Kansas City, MO  64110, United States; Munday, M., Cardiff Business School, Cardiff University, Colum Drive, Cardiff, CF10 3EU, United Kingdom; Roberts, A., Cardiff Business School, Cardiff University, Colum Drive, Cardiff, CF10 3EU, United Kingdom; Turner, K., School of Management and Languages, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom","The paper examines a method to attribute hazardous waste streams to regional production and consumption activity, and to connect these same waste streams through to different management options. We argue that a method using an input-output framework provides useful intelligence for decision makers seeking to connect elements of the management of the hazardous waste hierarchy to production and to different patterns and types of final consumption (of which domestic household consumption is one). This paper extends application of conventional demand driven input-output attribution methods to identify hazardous waste 'hotspots' in the supply chains of different final consumption goods and consumption groups. Using a regional case study to exposit the framework and its use, we find that domestic government final consumption of public administration production indirectly drives hazardous waste generation that goes to landfill, particularly in the domestic construction and sanitary services sectors, but also in the manufacture of wood products. © 2014 Elsevier B.V. All rights reserved.","Decision support systems; Hazardous waste management; Input-output tables; Supply chain management","Artificial intelligence; Chains; Decision making; Decision support systems; Decision tables; Hazardous materials; Hazards; Industrial wastes; Manufacture; Public administration; Supply chain management; Hazardous waste generation; Hazardous waste management; Hazardous wastes; Household Consumption; Input-output table; Management options; Production and consumption; Services sectors; Waste management","Economic and Social Research Council, ESRC: RES-066-27-0029","This paper draws on earlier research funded by the UK Economic and Social Research Council (Climate Change Leadership Fellowship, ESRC Grant Reference: RES-066-27-0029). We are also grateful to the UK Environment Agency for the provision of data on hazardous wastes.",,,,,,,,,,"Munday, M.; Cardiff Business School, Colum Drive, United Kingdom",,"Elsevier B.V.",03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85028095419
"Guo Z.X., Ngai E.W.T., Yang C., Liang X.","56040146300;7003298974;56286022200;46461488900;","An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment",2015,"International Journal of Production Economics","159",,,"16","28",,148,"10.1016/j.ijpe.2014.09.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84915733989&doi=10.1016%2fj.ijpe.2014.09.004&partnerID=40&md5=a7fc7c4b3c893483953b3553775ca68a","Business School, Sichuan University, Chengdu, 610065, China; Department of Management and Marketing, Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong","Guo, Z.X., Business School, Sichuan University, Chengdu, 610065, China; Ngai, E.W.T., Business School, Sichuan University, Chengdu, 610065, China; Yang, C., Department of Management and Marketing, Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong; Liang, X., Business School, Sichuan University, Chengdu, 610065, China","Global manufacturing companies have some pressing needs to improve production visibility and decision-making performance by implementing effective production monitoring and scheduling. This paper proposes a radio frequency identification (RFID)-based intelligent decision support system architecture to handle production monitoring and scheduling in a distributed manufacturing environment. A pilot implementation of the architecture is reported in a distributed clothing manufacturing environment. RFID and cloud technologies were integrated for real-time and remote production capture and monitoring. Intelligent optimization techniques were also implemented to generate effective production scheduling solutions. A prototype system with remote monitoring and production scheduling functions was developed and implemented in a distributed manufacturing environment, which demonstrated the effectiveness of the architecture. The proposed architecture has good extensibility and scalability, which can easily be integrated with production decision-making as well as production and logistics operations in the supply chain. Lastly, this paper discusses the difficulties encountered and lessons learned during system implementation and the managerial implications of the proposed architecture. © 2014 Elsevier B.V. All rights reserved.","Cloud technology; Distributed monitoring and scheduling; Intelligent decision-making; Managerial implications","Architecture; Artificial intelligence; Decision making; Decision support systems; Managers; Manufacture; Monitoring; Production control; Radio frequency identification (RFID); Supply chains; Clothing manufacturing; Cloud technologies; Distributed manufacturing; Distributed monitoring; Intelligent decision support systems; Intelligent decision-making; Intelligent optimization technique; Managerial implications; Scheduling","National Natural Science Foundation of China, NSFC: 71302134, 71371130; Hong Kong Polytechnic University, PolyU; Sichuan University, SCU: SKYB201301, SKZX2013-DZ07","The authors are grateful for the constructive comments of the referees on an earlier version of this paper. The authors acknowledge the financial supports from the Sichuan University (Grant nos. SKYB201301 and SKZX2013-DZ07 ), the National Natural Science Foundation of China (Grant nos. 71302134 and 71371130 ) and The Hong Kong Polytechnic University (Grant no. YK73 ).",,,,,,,,,,"Guo, Z.X.; Business School, Sichuan UniversityChina",,"Elsevier",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-84915733989
"Rodger J.A.","7005395612;","Application of a Fuzzy Feasibility Bayesian Probabilistic Estimation of supply chain backorder aging, unfilled backorders, and customer wait time using stochastic simulation with Markov blankets",2014,"Expert Systems with Applications","41","16",,"7005","7022",,49,"10.1016/j.eswa.2014.05.012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903954851&doi=10.1016%2fj.eswa.2014.05.012&partnerID=40&md5=64010638b35deed4ccf6e83305abcc92","Indiana University of Pennsylvania, MIS and Decision Sciences, Eberly College of Business and Information Technology, Indiana, PA 15705, United States","Rodger, J.A., Indiana University of Pennsylvania, MIS and Decision Sciences, Eberly College of Business and Information Technology, Indiana, PA 15705, United States","Because supply chains are complex systems prone to uncertainty, statistical analysis is a useful tool for capturing their dynamics. Using data on acquisition history and data from case study reports, we used regression analysis to predict backorder aging using National Item Identification Numbers (NIINs) as unique identifiers. More than 56,000 NIINs were identified and used in the analysis. Bayesian analysis was then used to further investigate the NIIN component variables. The results indicated that it is statistically feasible to predict whether an individual NIIN has the propensity to become a backordered item. This paper describes the structure of a Bayesian network from a real-world supply chain data set and then determines a posterior probability distribution for backorders using a stochastic simulation based on Markov blankets. Fuzzy clustering was used to produce a funnel diagram that demonstrates that the Acquisition Advice Code, Acquisition Method Suffix Code, Acquisition Method Code, and Controlled Inventory Item Code backorder performance metric of a trigger group dimension may change dramatically with variations in administrative lead time, production lead time, unit price, quantity ordered, and stock. Triggers must be updated regularly and smoothly to keep up with the changing state of the supply chain backorder trigger clusters of market sensitiveness, collaborative process integration, information drivers, and flexibility. © 2014 Elsevier Ltd. All rights reserved.","Backorder; Bayesian network; Decision support system; Fuzzy logic; Markov blanket; Supply chain","Artificial intelligence; Bayesian networks; Codes (symbols); Decision support systems; Fuzzy logic; Probability distributions; Regression analysis; Stochastic models; Backorders; Collaborative process; Identification number; Markov Blankets; Performance metrices; Probabilistic estimation; Stochastic simulations; Unique identifiers; Supply chains",,,,,,,,,,,,"Rodger, J.A.; Indiana University of Pennsylvania, MIS and Decision Sciences, Eberly College of Business and Information Technology, Indiana, PA 15705, United States; email: jrodger@iup.edu",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84903954851
"Mascle C., Gosse J.","6602564862;55796235600;","Inventory management maximization based on sales forecast: Case study",2014,"Production Planning and Control","25","12",,"1039","1057",,11,"10.1080/09537287.2013.805343","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901782036&doi=10.1080%2f09537287.2013.805343&partnerID=40&md5=ba4c96e8bbc1507a4e0fca61a4e1a12c","Department of Mechanical Engineering, Ecole Polytechnique of Montréal, Montréal, QC, Canada","Mascle, C., Department of Mechanical Engineering, Ecole Polytechnique of Montréal, Montréal, QC, Canada; Gosse, J., Department of Mechanical Engineering, Ecole Polytechnique of Montréal, Montréal, QC, Canada","The major purpose of this paper is to present a concept for reliable planning of the sales of small firms, where the large number of product variants complicates the implementation of this kind of system considerably. First, a methodology is presented to set up sales forecasting so that it can be integrated into the inventory management process. This inventory management software interprets forecasting information and provides users with a decision support system to minimize stocks in stores while at the same time avoiding missed sales. It is best applied in company types requiring high precision inventories, notably those in the textile industry; a large range of patterns are produced with many small variations (colors, size, customizations, etc.) and these products have a limited lifetime. Inventory management is difficult due to the multitude of products to account for and the necessity to sell them quickly. The methodology is intended for inventory management at the end of the supply chain. In store, the number of references, their similarities and the necessity to minimize unsold stock greatly complicates the reordering and restocking process. These types of companies do not easily lend themselves to classic techniques of sales forecasting and require specialized methods to estimate their needs precisely. © 2013 Taylor and Francis.","inventory management; range of patterns; sales forecast","Artificial intelligence; Decision support systems; Forecasting; Sales; Supply chains; Textile industry; Classic techniques; Inventory management; Inventory management process; Product variants; range of patterns; Sales forecasting; Sales forecasts; Small variations; Inventory control",,,,,,,,,,,,"Mascle, C.; Department of Mechanical Engineering, Ecole Polytechnique of Montréal, Montréal, QC, Canada; email: christian.mascle@polymtl.ca",,"Taylor and Francis Ltd.",09537287,,PPCOE,,"English","Prod Plann Control",Article,"Final","",Scopus,2-s2.0-84901782036
"Daaboul J., Castagna P., Da Cunha C., Bernard A.","36695950800;6602633253;18036803100;56517572500;","Value network modelling and simulation for strategic analysis: A discrete event simulation approach",2014,"International Journal of Production Research","52","17",,"5002","5020",,20,"10.1080/00207543.2014.886787","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905861954&doi=10.1080%2f00207543.2014.886787&partnerID=40&md5=d9530fa154fbe3d51caff48ff1a2311f","Laboratoire Roberval UMR CNRS 7337, Department of Mechanical Systems Engineering, UTC - Université de Technologie de Compiègne, Compiègne, France; Ecole Centrale de Nantes, LUNAM Université, IRCCyN UMR CNRS 6597 France, Nantes Cedex 3, France","Daaboul, J., Laboratoire Roberval UMR CNRS 7337, Department of Mechanical Systems Engineering, UTC - Université de Technologie de Compiègne, Compiègne, France; Castagna, P., Ecole Centrale de Nantes, LUNAM Université, IRCCyN UMR CNRS 6597 France, Nantes Cedex 3, France; Da Cunha, C., Ecole Centrale de Nantes, LUNAM Université, IRCCyN UMR CNRS 6597 France, Nantes Cedex 3, France; Bernard, A., Ecole Centrale de Nantes, LUNAM Université, IRCCyN UMR CNRS 6597 France, Nantes Cedex 3, France","The survival of a company nowadays depends on answering to a customer-driven economy, and therefore relies on the performance of its entire network of partners. Competition is no longer among companies nor among supply chains, but rather between networks of companies which form a value network. Thus, the need has arisen to analyse the performance of a network of companies, and include the customer-perceived value in the strategic decision-making process. This paper proposes a framework and a tool to model, simulate and analyse a value network as a decision support system. The method extends the SimulValor approach and language. The discrete event simulation tool relies on a developed value network simulation library. This paper presents a case study in the shoemaking industry to validate the proposed approach. © 2014 Taylor & Francis.","enterprise modelling; performance evaluation; strategic decision; value; value network","Artificial intelligence; Decision support systems; Supply chains; Enterprise modelling; performance evaluation; Strategic decisions; value; Value network; Discrete event simulation",,,,,,,,,,,,"Daaboul, J.; Laboratoire Roberval UMR CNRS 7337, Department of Mechanical Systems Engineering, UTC - Université de Technologie de Compiègne, Compiègne, France; email: Joanna.daaboul@utc.fr",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-84905861954
"Kumar S.","55616292300;","A knowledge based reliability engineering approach to manage product safety and recalls",2014,"Expert Systems with Applications","41","11",,"5323","5339",,29,"10.1016/j.eswa.2014.03.007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898412143&doi=10.1016%2fj.eswa.2014.03.007&partnerID=40&md5=80bc28d5fbbc61c1cea19ce6919f667a","Opus College of Business, University of St. Thomas, 1000 LaSalle Avenue, Minneapolis, MN 55403-2005, United States","Kumar, S., Opus College of Business, University of St. Thomas, 1000 LaSalle Avenue, Minneapolis, MN 55403-2005, United States","Purpose Managing processed food products' safety and recall is a challenge for industry and governments. Contaminated food items can create a significant public health hazard with potential for acute and chronic food borne illnesses. This industry study examines the challenges companies face while managing a processed food recall situation and devise a responsive and reliable knowledge management framework for product safety and recall supply chain for the focal global manufacturing and distribution enterprise. Method Drawing upon the knowledge management and product safety and recall literature and reliability engineering theory, this study uses a holistic single case based approach to develop a knowledge management framework with Failure Mode Effects and Criticality Analysis (FMECA) decision model. This knowledge management decision framework facilitates analysis of the root causes for each potential major recall issue and assesses the reliability of the product safety and recall supply chain system and its critical components. Results The main reasons highlighted for a recall and associated failure modes in a knowledge management framework are to devise appropriate deployment of resources, technology and procedures to recall supply chain. This study underscores specific information described by managers of a global processed food manufacturer and their perspectives about the product safety and recall process, and its complexities. Full scale implementation of product safety and recall supply chain in the proposed knowledge management framework after the current pilot study will be carried out eventually through expert systems. This operational system when fully implemented will capture the essence of decision making environments comprising goals and objectives, courses of action, resources, constraints, technology and procedures. Implications The study recognizes the significance of communication, integration, failsafe knowledge management process design framework, leveraging technology such as Radio Frequency Identification (RFID) within all levels of supply chain for product traceability and the proactive steps to help companies successfully manage a recall process and also reestablish trust among the consumers. The proposed knowledge management framework can also preempt product recall by acting as an early warning system. A formal knowledge management framework will enable a company's knowledge be cumulative for product safety and recall and serve as an important integrating and coordinating role for the organization. © 2014 Elsevier Ltd. All rights reserved.","Collaborative networks; Criticality index; Decision support system; FMECA; Knowledge management; Product safety and recall; Reliability; Reverse logistics","Accident prevention; Artificial intelligence; Criticality (nuclear fission); Decision support systems; Expert systems; Failure modes; Health hazards; Industry; Manufacture; Processed foods; Radio frequency identification (RFID); Reliability; Reliability analysis; Reliability theory; Supply chains; Collaborative network; FMECA; Full-scale implementation; Knowledge management framework; Knowledge management process; Product safety; Reliability engineering; Reverse logistics; Knowledge management",,,,,,,,,,,,"Kumar, S.; Opus College of Business, University of St. Thomas, 1000 LaSalle Avenue, Minneapolis, MN 55403-2005, United States; email: sameerkumar724@gmail.com",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84898412143
"Holimchayachotikul P., Derrouiche R., Damand D., Leksakul K.","36185507000;24474085500;6506317737;36052105700;","Value creation through collaborative supply chain: Holistic performance enhancement road map",2014,"Production Planning and Control","25","11",,"912","922",,13,"10.1080/09537287.2013.780313","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901788235&doi=10.1080%2f09537287.2013.780313&partnerID=40&md5=cf751c4245810622c8b8c2db5e164d45","Faculty of Engineering, Department of Industrial Engineering, Chiang Mai University, Chiang Mai 50200, Thailand; Saint-Etienne School of Management ESC-UMR 5600, 51-52 Cours Fauriel, Saint Etienne, 42009, France; HuManis-EM Strasbourg Business School, 61 avenue de la Forêt Noire, Strasbourg, F-67085, France","Holimchayachotikul, P., Faculty of Engineering, Department of Industrial Engineering, Chiang Mai University, Chiang Mai 50200, Thailand; Derrouiche, R., Saint-Etienne School of Management ESC-UMR 5600, 51-52 Cours Fauriel, Saint Etienne, 42009, France; Damand, D., HuManis-EM Strasbourg Business School, 61 avenue de la Forêt Noire, Strasbourg, F-67085, France; Leksakul, K., Faculty of Engineering, Department of Industrial Engineering, Chiang Mai University, Chiang Mai 50200, Thailand","This paper proposes an integrated novel framework between B2B-SCM using data mining techniques such as K-Means based on particle swarm intelligence (particle swarm optimisation) and association rule. It constructs relationship rules of holistic performance enhancement road map. The data-set of relationships between enterprise and its direct customers of the case study organisations in France was used for demonstration. The experiment results show how domain managers powerfully utilise the graphical analysis results to provide the holistic performance improvement and weakness resolution relationship rules. In the long run, organisations are able to use this framework to design and adjust their units to conform the exact customer needs. This paper introduces and explains a new idea of measuring value added along the supply chain from a collaborative perspective. The extended model is adapted from our previous model and from balanced scorecard model. It provides a tool to measure tangible and intangible value between partners. © 2013 Taylor and Francis.","association rules; holistic performance enhancement road map K-Means; particle swarm intelligence; supply chain collaboration; value creation","Artificial intelligence; Association rules; Particle swarm optimization (PSO); Supply chains; Balanced scorecards; Collaborative supply chains; Particle swarm; Particle swarm optimisation; Performance enhancements; Road-maps; Supply chain collaboration; Value creation; Maps","Thailand Research Fund, TRF: PHD/0090/2553","Financial support was provided by the Thailand Research Fund (TRF) through the Royal Golden Jubilee (RGJ) PhD Program (PHD/0090/2553).",,,,,,,,,,"Derrouiche, R.; Saint-Etienne School of Management ESC-UMR 5600, 51-52 Cours Fauriel, Saint Etienne, 42009, France; email: ridha_derrouiche@esc-saint-etienne.fr",,"Taylor and Francis Ltd.",09537287,,PPCOE,,"English","Prod Plann Control",Article,"Final","",Scopus,2-s2.0-84901788235
"Boza A., Alemany M.M.E., Alarcón F., Cuenca L.","24482977800;15841436200;23666504600;24482988600;","A model-driven DSS architecture for delivery management in collaborative supply chains with lack of homogeneity in products",2014,"Production Planning and Control","25","8",,"650","661",,13,"10.1080/09537287.2013.798085","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897917176&doi=10.1080%2f09537287.2013.798085&partnerID=40&md5=2a5c33f915d8a4dde9bea1fb87e6f8bf","Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain","Boza, A., Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain; Alemany, M.M.E., Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain; Alarcón, F., Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain; Cuenca, L., Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain","Uniform product deliveries are required in the ceramic, horticulture and leather sectors because customers require product homogeneity to use, present or consume them together. Some industries cannot prevent the lack of homogeneity in products in their manufacturing processes; hence, they cannot avoid non-uniform finished products arriving at their warehouses and, consequently, fragmentation of their stocks. Therefore, final uniform product amounts do not match planned production ones, which frequently makes serving previous committed orders with homogeneous quantities impossible. This paper proposes a model-driven decision support system (DSS) to help the person in charge of delivery management to reallocate the available real inventory to orders to satisfy homogenous customer requirements in a collaborative supply chain (SC). The DSS has been validated in a ceramic tile collaborative SC. © 2013 © 2013 Taylor & Francis.","Collaborative supply chain; Decision support system; Delivery management; Lack of homogeneity in products","Artificial intelligence; Customer satisfaction; Decision support systems; Collaborative supply chains; Customer requirements; Decision support system (dss); Delivery management; Finished products; Lack of homogeneity in products; Manufacturing process; Product delivery; Supply chains","Ministerio de Economía y Competitividad, MINECO: DPI2011-23597; Universitat Politècnica de València, UPV: PAID-06-11/1840","This research has been carried out within the framework of the project funded by the Spanish Ministry of Economy and Competitiveness (Ref. DPI2011-23597) and the Polytechnic University of Valencia (Ref. PAID-06-11/1840) entitled ‘Methods and models for operations planning and order management in supply chains characterized by uncertainty in production due to the lack of product uniformity’ (PLANGES-FHP). Also, we thank the comments and suggestions made by the Editors and the Reviewers. In our opinion, these changes have improved the quality of the paper.",,,,,,,,,,"Boza, A.; Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain; email: aboza@cigip.upv.es",,"Taylor and Francis Ltd.",09537287,,PPCOE,,"English","Prod Plann Control",Article,"Final","All Open Access, Green",Scopus,2-s2.0-84897917176
"Theißen S., Spinler S.","56030228700;7006502092;","Strategic analysis of manufacturer-supplier partnerships: An ANP model for collaborative CO2 reduction management",2014,"European Journal of Operational Research","233","2",,"383","397",,77,"10.1016/j.ejor.2013.08.023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84889820080&doi=10.1016%2fj.ejor.2013.08.023&partnerID=40&md5=47958435137423353992473b807476e3","Kühne Foundation Endowed Department of Logistics Management, WHU, Otto Beisheim School of Management, Burgplatz 2, 56179 Vallendar, Germany","Theißen, S., Kühne Foundation Endowed Department of Logistics Management, WHU, Otto Beisheim School of Management, Burgplatz 2, 56179 Vallendar, Germany; Spinler, S., Kühne Foundation Endowed Department of Logistics Management, WHU, Otto Beisheim School of Management, Burgplatz 2, 56179 Vallendar, Germany","The objective of this manuscript is to introduce a decision methodology that allows manufacturing firms to evaluate which supplier is the most suitable partner for the implementation of a collaborative CO2 reduction management approach. The decision problem is developed for the fast-moving consumer goods (FMCGs) industry, which currently ranks among the ten largest CO2 emitting industries worldwide. In this paper, the evaluation and selection of the most suitable supplier is performed using the analytic network process (ANP), a decision-making technique that allows practitioners to solve complex decision structures. The key contributions of the present paper reside in the combination of literature and case-based derived decision criteria, aimed at enhancing judgment validity, with particular emphasis on a collaborative setting, which is highly relevant in the present context as the focal firms often lack the necessary skills for sustainability and, at the same time, are responsible for sustainability in the supply chain. The practical application of the ANP model at a major FMCG company yields robust results corroborated through a sensitivity analysis. © 2013 Elsevier B.V. All rights reserved.","Analytic network process; Decision support systems; Environment; Multiple criteria analysis; Supply chain management","Artificial intelligence; Carbon dioxide; Decision support systems; Decision theory; Manufacture; Sensitivity analysis; Supply chain management; Sustainable development; Analytic network process; Collaborative settings; Decision criterions; Decision methodology; Environment; Fast moving consumer goods; Manufacturing firms; Multiple criteria analysis; Decision making",,,,,,,,,,,,"Theißen, S.; Kühne Foundation Endowed Department of Logistics Management, Burgplatz 2, 56179 Vallendar, Germany; email: Sebastian.Theissen@whu.edu",,"Elsevier B.V.",03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","",Scopus,2-s2.0-84889820080
"Kallestrup K.B., Lynge L.H., Akkerman R., Oddsdottir T.A.","56400943600;56401010500;16315223800;55790574400;","Decision support in hierarchical planning systems: The case of procurement planning in oil refining industries",2014,"Decision Support Systems","68",,,"49","63",,16,"10.1016/j.dss.2014.09.003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84911902226&doi=10.1016%2fj.dss.2014.09.003&partnerID=40&md5=be93d8a0bf86cbdf8da37d7e8be6c815","Department of Management Engineering, Technical University of Denmark, Produktionstorvet 424, Kgs. Lyngby, Copenhagen, 2800, Denmark; TUM School of Management, Technische Universität München, Arcisstr. 21, Munich, 80333, Germany","Kallestrup, K.B., Department of Management Engineering, Technical University of Denmark, Produktionstorvet 424, Kgs. Lyngby, Copenhagen, 2800, Denmark; Lynge, L.H., Department of Management Engineering, Technical University of Denmark, Produktionstorvet 424, Kgs. Lyngby, Copenhagen, 2800, Denmark; Akkerman, R., TUM School of Management, Technische Universität München, Arcisstr. 21, Munich, 80333, Germany; Oddsdottir, T.A., Department of Management Engineering, Technical University of Denmark, Produktionstorvet 424, Kgs. Lyngby, Copenhagen, 2800, Denmark","In this paper, we discuss the development of decision support systems for hierarchically structured planning approaches, such as commercially available advanced planning systems. We develop a framework to show how such a decision support system can be designed with the existing organization in mind, and how a decision process and corresponding software can be developed from this basis. Building on well-known hierarchical planning concepts, we include the typical anticipation mechanisms used in such systems to be able to decompose planning problems, both from the perspective of the planning problem and from the perspective of the organizational aspects involved. To exemplify and develop our framework, we use a case study of crude oil procurement planning in the refining industry. The results of the case study indicate an improved organizational embedding of the DSS, leading to significant savings in terms of planning efforts and procurement costs. In general, our framework aims to support the continuous improvement of advanced planning systems, increasing planning quality in complex supply chain settings. © 2014 Elsevier B.V. All rights reserved.","Advanced planning systems; Crude oil operations; Hierarchical planning; Procurement planning","Artificial intelligence; Crude oil; Hierarchical systems; Oil shale; Petroleum refining; Refining; Supply chains; Advanced planning system; Complex supply chain; Continuous improvements; Crude oil operations; Hierarchical planning; Oil-refining industry; Organizational aspects; Procurement costs; Decision support systems",,,,,,,,,,,,"Akkerman, R.; TUM School of Management, Technische Universität München, Arcisstr. 21, Germany",,"Elsevier",01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-84911902226
"Manzini R., Accorsi R., Bortolini M.","6701312938;54779320500;54779261700;","Operational planning models for distribution networks",2014,"International Journal of Production Research","52","1",,"89","116",,31,"10.1080/00207543.2013.828168","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906946358&doi=10.1080%2f00207543.2013.828168&partnerID=40&md5=51493ba61c01218ec1b061e7d34bf91f","Department of Industrial Engineering, Alma Mater Studiorum-University of Bologna, Bologna, Italy","Manzini, R., Department of Industrial Engineering, Alma Mater Studiorum-University of Bologna, Bologna, Italy; Accorsi, R., Department of Industrial Engineering, Alma Mater Studiorum-University of Bologna, Bologna, Italy; Bortolini, M., Department of Industrial Engineering, Alma Mater Studiorum-University of Bologna, Bologna, Italy","The design and optimisation of a logistic network deals with a wide set of decisions, e.g. the determination of the best location and capacity of the different logistic facilities (production plants, distribution centres, transit points, wholesalers, etc.), the allocation of the product demand coming from customers in presence (or absence) of fractionable flows of material, the determination of the best transportation mode (truck, rail, etc.) as well as loading and routing of vehicles. These decisions involve multiple stages of a distribution network: customers-regional distribution centres (RDC), RDCs-central distribution centres (CDC) and CDCs-production plants and sources, in presence of multiple products and the variable time (i.e. time-dependent product demand and flows of material). This paper presents a top-down methodology that joins the strategic planning, the tactical planning and the operational planning of distribution networks with a special focus on the development of effective heuristic methods to face the vehicle routing problem. Original models and heuristic algorithms for the operational planning are illustrated. The impact of the strategic and tactical decisions on the performance of the operational planning is evaluated by the application of the proposed hierarchical approach to two realistic case studies. Obtained results are illustrated in a what-if experimental analysis conducted on multiple problem settings and realistic scenarios. © 2013 Taylor & Francis.","Decision support system (DSS); Facility layout; Location allocation problem (LAP); Mixed integer linear programming; Supply chain (SC); Transportation; Vehicle routing","Artificial intelligence; Decision support systems; Heuristic algorithms; Heuristic methods; Integer programming; Product design; Supply chains; Transportation; Vehicle routing; Warehouses; Decision support system (dss); Experimental analysis; Facility layout; Hierarchical approach; Location allocation problem; Mixed integer linear programming; Operational planning; Vehicle Routing Problems; Loading",,,,,,,,,,,,"Manzini, R.; Department of Industrial Engineering, Alma Mater Studiorum-University of Bologna, Bologna, Italy; email: riccardo.manzini@unibo.it",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-84906946358
"Roozbeh Nia A., Hemmati Far M., Akhavan Niaki S.T.","35105822800;55817196900;57193316997;","A fuzzy vendor managed inventory of multi-item economic order quantity model under shortage: An ant colony optimization algorithm",2014,"International Journal of Production Economics","155",,,"259","271",,53,"10.1016/j.ijpe.2013.07.017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906081699&doi=10.1016%2fj.ijpe.2013.07.017&partnerID=40&md5=47f79854677d4d32690cd2d0bb24357b","Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran; Department of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran; Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran","Roozbeh Nia, A., Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran; Hemmati Far, M., Department of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran; Akhavan Niaki, S.T., Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran","In this study, a multi-item economic order quantity model with shortage under vendor managed inventory policy in a single vendor single buyer supply chain is developed. This model explicitly includes warehouse capacity and delivery constraints, bounds order quantity, and limits the number of pallets. Not only the demands are considered imprecise, but also resources such as available storage and total order quantity of all items can be vaguely defined in different ways. An ant colony optimization is employed to find a near-optimum solution of the fuzzy nonlinear integer-programming problem with the objective of minimizing the total cost of the supply chain. Since no benchmark is available in the literature, a genetic algorithm and a differential evolution are developed as well to validate the result obtained. Furthermore, the applicability of the proposed methodology along with a sensitivity analysis on its parameter is demonstrated using five numerical examples containing different numbers of items. © 2014 Elsevier B.V. All rights reserved.","Ant colony optimization; Differential evolution; Economic order quantity; Fuzzy nonlinear integer programming; Genetic algorithm; Vendor managed inventory","Algorithms; Ant colony optimization; Artificial intelligence; Genetic algorithms; Integer programming; Numerical methods; Supply chains; Ant colony optimization; Artificial intelligence; Chains; Genetic algorithms; Integer programming; Numerical methods; Sensitivity analysis; Supply chains; Ant Colony Optimization algorithms; Delivery constraints; Differential Evolution; Economic order quantity; Economic order quantity models; Non-linear integer programming; Vendor managed Inventory; Warehouse capacity; Economic analysis; Economic analysis",,,,,,,,,,,,"Roozbeh Nia, A.; Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran; email: ali.roozbehnia@gmail.com",,"Elsevier",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-84906081699
"Yang W., Fung R.Y.K.","56021113000;7101741904;","An available-to-promise decision support system for a multi-site make-to-order production system",2014,"International Journal of Production Research","52","14",,"4253","4266",,13,"10.1080/00207543.2013.877612","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902797021&doi=10.1080%2f00207543.2013.877612&partnerID=40&md5=e350ec9315af297c4477239b16a37a82","Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong","Yang, W., Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong; Fung, R.Y.K., Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong","Available-to-promise (ATP) decision, as a means for managing customer demands, production scheduling and the available resource, has three main components: order acceptance/selection, due date assignment and order scheduling. This research presents two decision support systems of hierarchical and monolithic models to integrate the three ATP components to maximise the profit, while satisfying customer orders over required time horizon and effective cost in a multi-site make-to-order supply chain scenario. Numerical examples are used to demonstrate the application of the models and their effectiveness. In order to improve the system efficiency, a branch-and-price approach is adopted to solve the proposed monolithic model. © 2012 Taylor & Francis.","Available-to-promise; Branch-and-price; Make-to-order production; Order promising","Decision support systems; Integer programming; Production control; Production engineering; Supply chains; Available-to-promise; Branch and price; Due-date assignment; Make-to-order production systems; Make-to-order productions; Order promising; Production Scheduling; System efficiency; Artificial intelligence","CityU 113609","The work described in this paper was supported by a Research Grant from Government Research Fund (GRF) of Hong Kong (RGC # CityU 113609).",,,,,,,,,,"Fung, R.Y.K.; Department of Systems Engineering and Engineering Management, City University of Hong KongHong Kong; email: richard.fung@cityu.edu.hk",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-84902797021
"Ting S.L., Tse Y.K., Ho G.T.S., Chung S.H., Pang G.","35186697100;35304027000;8348673600;36023203100;55980882400;","Mining logistics data to assure the quality in a sustainable food supply chain: A case in the red wine industry",2014,"International Journal of Production Economics","152",,,"200","209",,75,"10.1016/j.ijpe.2013.12.010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899916498&doi=10.1016%2fj.ijpe.2013.12.010&partnerID=40&md5=1054ed666a6ce2ac21f40dace4ec3d08","Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong; York Management School, University of York, Freboys Lane, Heslington, York YO10 5GD, United Kingdom; Newcastle University Business School, 5 Barrack Road, Newcastle-upon Tyne NE1 4SE, United Kingdom","Ting, S.L., Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong; Tse, Y.K., York Management School, University of York, Freboys Lane, Heslington, York YO10 5GD, United Kingdom; Ho, G.T.S., Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong; Chung, S.H., Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong; Pang, G., Newcastle University Business School, 5 Barrack Road, Newcastle-upon Tyne NE1 4SE, United Kingdom","In recent years, food supply chains have faced increased quality risk, caused by the extended global supply chain and increased consumer demands on quality and safety. Given the concern regarding quality sustainability in the food supply chain, much attention is being paid to continuous planning and monitoring of quality assurance practices in the supply chain network. In this research, we propose a supply chain quality sustainability decision support system (QSDSS), adopting association rule mining and Dempster's rule of combination techniques. The aim of QSDSS is to support managers in food manufacturing firms to define good logistics plans in order to maintain the quality and safety of food products. We conduct a case study of a Hong Kong red wine company in order to illustrate the applicability and effectiveness of QSDSS. Implications of the proposed approach are discussed, and suggestions for future work are outlined. © 2013 Elsevier B.V.","Association rule; Quality sustainability; Supply chain quality; Wine industry","Artificial intelligence; Association rules; Decision support systems; Food supply; Industry; Quality assurance; Sustainable development; Wine; Continuous planning; Dempster's rule of combination; Food manufacturing; Global supply chain; Quality and safeties; Quality assurance practices; Supply chain network; Wine industry; Supply chains","Chartered Institute of Logistics and Transport, CILT","This research was supported in part by the Seed Corn Research Fund - CILT(UK) 2013.",,,,,,,,,,"Tse, Y.K.; York Management School, University of York, Freboys Lane, Heslington, York YO10 5GD, United Kingdom; email: mike.tse@york.ac.uk",,"Elsevier",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-84899916498
"Groves W., Collins J., Gini M., Ketter W.","57192812720;55461319800;7006205599;6506124372;","Agent-assisted supply chain management: Analysis and lessons learned",2014,"Decision Support Systems","57","1",,"274","284",,31,"10.1016/j.dss.2013.09.006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892370083&doi=10.1016%2fj.dss.2013.09.006&partnerID=40&md5=708382db3f92e682b569323a1c33dca5","Department of Computer Science and Engineering, University of Minnesota, United States; Rotterdam School of Management, Erasmus University, Netherlands","Groves, W., Department of Computer Science and Engineering, University of Minnesota, United States; Collins, J., Department of Computer Science and Engineering, University of Minnesota, United States; Gini, M., Department of Computer Science and Engineering, University of Minnesota, United States; Ketter, W., Rotterdam School of Management, Erasmus University, Netherlands","This work explores ""big data"" analysis in the context of supply chain management. Specifically we propose the use of agent-based competitive simulation as a tool to develop complex decision making strategies and to stress test them under a variety of market conditions. We propose an extensive set of business key performance indicators (KPIs) and apply them to analyze market dynamics. We present these results through statistics and visualizations. Our testbed is a competitive simulation, the Trading Agent Competition for Supply-Chain Management (TAC SCM), which simulates a one-year product life-cycle where six autonomous agents compete to procure component parts and sell finished products to customers. The paper provides analysis techniques and insights applicable to other supply chain environments. © 2013 Elsevier B.V.","Decision support systems; Economic simulation; Key performance indicators; Software agents; Supply chain management; Trading Agent Competition","Analysis techniques; Complex decision; Economic simulation; Finished products; Key performance indicators; Market condition; Market dynamics; Trading Agent Competition; Artificial intelligence; Autonomous agents; Benchmarking; Decision support systems; Software agents; Supply chain management; Commerce",,,,,,,,,,,,"Groves, W.; Department of Computer Science and Engineering, United States; email: groves@cs.umn.edu",,,01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-84892370083
"Miao Z., Cai S., Xu D.","14037532500;25031023900;55805737200;","Applying an adaptive tabu search algorithm to optimize truck-dock assignment in the crossdock management system",2014,"Expert Systems with Applications","41","1",,"16","22",,22,"10.1016/j.eswa.2013.07.007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885174289&doi=10.1016%2fj.eswa.2013.07.007&partnerID=40&md5=106d0febb8c3f5a8cc72572115d09a83","School of Management, Xiamen University, Xiamen 361005, China","Miao, Z., School of Management, Xiamen University, Xiamen 361005, China; Cai, S., School of Management, Xiamen University, Xiamen 361005, China; Xu, D., School of Management, Xiamen University, Xiamen 361005, China","Different from warehouse, crossdock is considered as a ""JIT"" technique in logistics and supply chain management (LSCM), which is usually a short period of time to store cargos. Since cargos transported to a crossdock by inbound trucks are immediately sorted out, repackaged, routed and loaded into outbound trucks and then delivered to customers within one day, one of the key issues to operate crossdock successfully is to develop an efficient truck-dock assignment module in the crossdock management system so that all the cargos can be delivered to customers on time. In this paper, we propose an adaptive tabu search (ATS) algorithm as an artificial intelligence (AI) tool to optimize the truck-dock assignment problem within a crossdock in a very efficient way, and computational experiments are conducted, showing that our approach dominates the CPLEX Solver in both effectiveness and efficiency. © 2013 Elsevier Ltd. All rights reserved.","Artificial intelligence; Crossdock management system; Dock assignment; Tabu search","Adaptive tabu search; Assignment problems; Computational experiment; Effectiveness and efficiencies; Key Issues; Logistics and supply chain management; Management systems; Short periods; Artificial intelligence; Automobiles; Computational efficiency; Docks; Hydraulic structures; Management; Optimization; Supply chain management; Tabu search; Trucks","JA10001S; 2011R0081; National Natural Science Foundation of China, NSFC: 70802052, 71171171, 71202059; Ministry of Education of the People's Republic of China, MOE: NCET-10-0712; Fundamental Research Funds for the Central Universities: 2010221025, 2011221016, 2012221011; Specialized Research Fund for the Doctoral Program of Higher Education of China, SRFDP: 20100121120013","The authors thank the Editors and anonymous referees for their valuable comments and suggestions that have greatly improved this article. This research was supported in part by National Natural Science Foundation of China (70802052, 71171171, 71202059), the Fundamental Research Funds for the Central Universities (2012221011, 2011221016, 2010221025), MOE (NCET-10-0712), the Academic Outstanding Youthful Research Talent Plan of Fujian Province (JA10001S), Soft Science Projects of Fujian Province (2011R0081), and Research Fund for the Doctoral Program of Higher Eduction of China (20100121120013).",,,,,,,,,,"Cai, S.; School of Management, , Xiamen 361005, China; email: caishun@xmu.edu.cn",,,09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84885174289
"Zhou W., Piramuthu S.","8448821700;6701837203;","Remanufacturing with RFID item-level information: Optimization, waste reduction and quality improvement",2013,"International Journal of Production Economics","145","2",,"647","657",,51,"10.1016/j.ijpe.2013.05.019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883461226&doi=10.1016%2fj.ijpe.2013.05.019&partnerID=40&md5=e38976dcd6992447e79b63f3c5dc0415","Information and Operations Management, ESCP Europe, Paris, France; Information Systems and Operations Management, University of Florida, Gainesville, FL 32611, United States; RFID European Lab, Paris, France","Zhou, W., Information and Operations Management, ESCP Europe, Paris, France, RFID European Lab, Paris, France; Piramuthu, S., Information Systems and Operations Management, University of Florida, Gainesville, FL 32611, United States, RFID European Lab, Paris, France","We consider RFID tags and their applications from a recycling/ remanufacturing perspective and propose a novel framework to assist such process based on item-level information visibility and instantaneous tracking/tracing ability enabled by RFID. The incorporation of RFID in the reverse supply chain results in cost reduction, service and production quality improvement and pollution and waste reduction. With RFID in a reverse supply chain, we observe the power shift from waste-driven to market-driven system. Moreover, RFID's value increases with uncertainties in reverse operations as well as individual products and components. © 2013 Elsevier B.V. All rights reserved.","Closed-loop supply chain; Decision support system; Knowledge-based system; Manufacturing; RFID","Closed-loop supply chain; Information visibilities; Process-based; Production quality; Quality improvement; Reverse operations; Reverse supply chains; Waste reduction; Artificial intelligence; Decision support systems; Knowledge based systems; Manufacture; Radio frequency identification (RFID); Supply chains; Linear matrix inequalities",,,,,,,,,,,,"Piramuthu, S.; Information Systems and Operations Management, , Gainesville, FL 32611, United States; email: selwyn@ufl.edu",,,09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-84883461226
"Latha Shankar B., Basavarajappa S., Kadadevaramath R.S., Chen J.C.H.","55260906700;12141008400;26324982000;10243764200;","A bi-objective optimization of supply chain design and distribution operations using non-dominated sorting algorithm: A case study",2013,"Expert Systems with Applications","40","14",,"5730","5739",,29,"10.1016/j.eswa.2013.03.047","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878276990&doi=10.1016%2fj.eswa.2013.03.047&partnerID=40&md5=b2569782ea18aac3c5fe2cc2dce4d7f6","Department of Industrial Engineering and Management, Siddaganga Institute of Technology, Tumkur 572 103, Karnataka, India; Department of Studies in Mechanical Engineering, University B.D.T. College of Engineering, Davangere 577 004, Karnataka, India; Graduate School of Business, Gonzaga University, Spokane, WA, United States","Latha Shankar, B., Department of Industrial Engineering and Management, Siddaganga Institute of Technology, Tumkur 572 103, Karnataka, India; Basavarajappa, S., Department of Studies in Mechanical Engineering, University B.D.T. College of Engineering, Davangere 577 004, Karnataka, India; Kadadevaramath, R.S., Department of Industrial Engineering and Management, Siddaganga Institute of Technology, Tumkur 572 103, Karnataka, India; Chen, J.C.H., Graduate School of Business, Gonzaga University, Spokane, WA, United States","This paper considers simultaneous optimization of strategic design and distribution decisions for three-echelon supply chain architecture consisting of following three players; suppliers, production plants, and distribution centers (DCs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to distribution centers, so as to minimize the combined facility location, production, inventory, and shipment costs and maximize fill rate. To achieve this, three-echelon network model is mathematically represented and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization algorithm (MOHPSO). This heuristic incorporates non-dominated sorting (NDS) procedure to achieve bi-objective optimization of two conflicting objectives. The applicability of proposed optimization algorithm was then tested by applying it to standard test problems found in literature. On achieving comparable results, the approach was applied to actual data of a pump manufacturing industry. The results show that the proposed solution approach performs efficiently. © 2013 Elsevier Ltd. All rights reserved.","Bi-objective; Non-dominating sorting; Particle swarm; Supply chain; Swarm intelligence; Three-echelon","Bi objectives; Bi-objective optimization; Hybrid particle swarm optimization algorithm; Non-dominated sorting algorithms; Particle swarm; Supply chain architecture; Swarm Intelligence; Three-echelon; Algorithms; Artificial intelligence; Location; Particle swarm optimization (PSO); Screening; Ships; Warehouses; Supply chains",,,,,,,,,,,,"Chen, J.C.H.; Graduate School of Business, , Spokane, WA, United States; email: chen@jepson.gonzaga.edu",,,09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84878276990
"Banerjee S., Golhar D.Y.","7404545187;6603840813;","A decision support system for a third-party coordinator managing supply chain with demand uncertainty",2013,"Production Planning and Control","24","6",,"521","531",,6,"10.1080/09537287.2011.639586","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877935255&doi=10.1080%2f09537287.2011.639586&partnerID=40&md5=87b45efe1ff4a56cae494cb6f9e9e1ca","School of Business, Rutgers University-Camden, 227 Penn Street, Camden, NJ 08102, United States; Hawoth College of Business, Western Michigan University, Kalamazoo, MI 49008, United States","Banerjee, S., School of Business, Rutgers University-Camden, 227 Penn Street, Camden, NJ 08102, United States; Golhar, D.Y., Hawoth College of Business, Western Michigan University, Kalamazoo, MI 49008, United States","Global supply chains reduce cost but increase lead times, complexities and uncertainties. Retailers in consumer products industry are getting shorter lead time to respond to market demand. To meet this challenge, many rely on third party supply chain managers (SCMs) for economically supplying required quantities of finished products quickly. However, due to shorter time to market, the SCM has to procure raw materials and start production process based on expected demand. Since SCM absorbs financial penalties associated with under-and over-estimation of demand from retailer, finding an optimal production lot size and product customisation strategy are essential to an SCM's operation. We develop a profit maximisation model and provide a close-form solution that allows an SCM to calculate optimal production lot size. The model is used to examine profitability of postponing product customisation. Finally, the effect of demand variation on SCM's profitability is explored. © 2013 Copyright Taylor and Francis Group, LLC.","customisation postponement; decision support system; demand uncertainty; supply chain manager","Customisation; Demand uncertainty; Demand variations; Finished products; Global supply chain; Optimal production; Product customisation; Production process; Artificial intelligence; Consumer products; Decision support systems; Managers; Marketing; Optimization; Product development; Profitability; Supply chains",,,,,,,,,,,,"Banerjee, S.; School of Business, 227 Penn Street, Camden, NJ 08102, United States; email: snehamy@crab.rutgers.edu",,,09537287,,PPCOE,,"English","Prod Plann Control",Article,"Final","",Scopus,2-s2.0-84877935255
"Dong C.-S.J., Srinivasan A.","35301543200;7202314405;","Agent-enabled service-oriented decision support systems",2013,"Decision Support Systems","55","1",,"364","373",,31,"10.1016/j.dss.2012.05.047","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877755057&doi=10.1016%2fj.dss.2012.05.047&partnerID=40&md5=fd33fcc8fe9e7f8566900ddbbf891cd1","University of Auckland Business School, OGG Building, 12 Grafton Road, Private Bag 92019, Auckland, New Zealand","Dong, C.-S.J., University of Auckland Business School, OGG Building, 12 Grafton Road, Private Bag 92019, Auckland, New Zealand; Srinivasan, A., University of Auckland Business School, OGG Building, 12 Grafton Road, Private Bag 92019, Auckland, New Zealand","The design of Decision Support Systems have recently emphasized web enablement as the next step in design improvements for this class of applications. We argue that these approaches fail to address the key notion of adaptability in the support for decision makers. Instead of focusing exclusively on automation in decision making, we believe it is also necessary to pay attention to the interplay between decision makers and organizational processes. The service oriented view of organizations recognizes the need to accommodate the changing reality of organizational dynamics. For example, the service science approach focuses on interactions between service providers, their clients, and consumers as important interacting components of a service system. Current approaches to DSS design are constrained in terms of their ability to adapt to changes in user requirements and to provide support for the evolution of systems. This situation worsens when resources are distributed at different locations across organizations, decision making processes are required to be integrated at different points in time, and when collaboration is needed among decision makers. However, this typically characterizes the needs of collaborative decision making in networked organizations as exemplified by systems used for supply chain management. To address these problems we leverage the power of services for designing a framework that explicitly recognizes the need for design based on service delivery. We develop an agent-enabled service-oriented architecture to realize the proposed framework with service and agent paradigms. The architecture is refined and validated with an implementation in the supply chain context. © 2012 Elsevier B.V. All rights reserved.","Decision making processes; Decision support systems; Service delivery; Services; Software agents","Collaborative decision making; Decision making process; Design improvements; Networked organizations; Organizational dynamics; Organizational process; Service delivery; Services; Artificial intelligence; Decision support systems; Design; Information services; Service oriented architecture (SOA); Societies and institutions; Software agents; Supply chain management; Decision making",,,,,,,,,,,,"Srinivasan, A.; University of Auckland Business School, 12 Grafton Road, Private Bag 92019, Auckland, New Zealand; email: a.srinivasan@auckland.ac.nz",,,01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-84877755057
"Lenny Koh S.C., Genovese A., Acquaye A.A., Barratt P., Rana N., Kuylenstierna J., Gibbs D.","24824748600;34970949600;34879448200;36636701300;55582064100;57194244669;55804460500;","Decarbonising product supply chains: Design and development of an integrated evidence-based decision support system-the supply chain environmental analysis tool (SCEnAT)",2013,"International Journal of Production Research","51","7",,"2092","2109",,63,"10.1080/00207543.2012.705042","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873477519&doi=10.1080%2f00207543.2012.705042&partnerID=40&md5=1a84552db3270b5badb1ca7e3eb5be75","Logistics and Supply Chain Management (LSCM) Research Centre, Centre for Energy, Environment and Sustainability (CEES), University of Sheffield, Sheffield, United Kingdom; Department of Geography, University of Hull, Kingston Upon Hull, Hull, United Kingdom; Stockholm Environment Institute, University of York, Grimston House, York, United Kingdom","Lenny Koh, S.C., Logistics and Supply Chain Management (LSCM) Research Centre, Centre for Energy, Environment and Sustainability (CEES), University of Sheffield, Sheffield, United Kingdom; Genovese, A., Logistics and Supply Chain Management (LSCM) Research Centre, Centre for Energy, Environment and Sustainability (CEES), University of Sheffield, Sheffield, United Kingdom; Acquaye, A.A., Logistics and Supply Chain Management (LSCM) Research Centre, Centre for Energy, Environment and Sustainability (CEES), University of Sheffield, Sheffield, United Kingdom; Barratt, P., Department of Geography, University of Hull, Kingston Upon Hull, Hull, United Kingdom; Rana, N., Logistics and Supply Chain Management (LSCM) Research Centre, Centre for Energy, Environment and Sustainability (CEES), University of Sheffield, Sheffield, United Kingdom; Kuylenstierna, J., Stockholm Environment Institute, University of York, Grimston House, York, United Kingdom; Gibbs, D., Department of Geography, University of Hull, Kingston Upon Hull, Hull, United Kingdom","Based upon an increasing academic and business interest in greening the industrial supply chains, this paper establishes the need for a state-of-the-art decision support system (DSS) for carbon emissions accounting and management, mainly across the product supply chains by identifying methodological shortcomings in existing tools, and proposing a supply chain (SC) framework which provide businesses with a holistic understanding of their supply chains and ensuring partners within supply chain collaborative networks have a shared understanding of their emissions. It describes the design and development of a DSS now known as supply chain environmental analysis tool (SCEnAT) in detail, putting its unique and innovative features into a comparative perspective vis-à-vis existing tools and software of different types. The methodological framework used to design and develop SCEnAT integrates different individual techniques/methods of supply chain (SC) mapping, SC carbon accounting, SC interventions and SC interventions evaluation on a range of key performance indicators (KPIs). These individual methods have been used and applied innovatively to the challenge of designing SCEnAT within the desired framework. Finally, we demonstrate the application of SCEnAT, especially the advantage of using a robust carbon accounting methodology, to a SC case study. The SCEnAT framework pushes the theoretical boundary by addressing the problems of intra-organisational approach in decision making for lowering carbon along the supply chain; with an open innovation, cutting edge, hybridised framework that considers the supply chain as a whole in co-decision making for lowering carbon along the supply chain with the most robust methodology of hybrid life cycle analysis (LCA) that considers direct and indirect emissions and interventional performance evaluation for low carbon technology investment and business case building in order to adapt and mitigate climate change problems. This research has implications for future sustainability research in SC, decisions science, management theory, practice and policy. © 2013 Taylor & Francis Group, LLC.","decision support system; SC carbon accounting; SC decarbonisation; SC low carbon interventions; SC management; SC mapping","Business case; Carbon accounting; Carbon emissions; Collaborative network; Cutting edges; Decision supports; Design and Development; Environmental analysis; Industrial supply; Interventional; Key performance indicators; Life cycle analysis; Low carbon; Low-carbon technologies; Management theory; Methodological frameworks; Open innovation; Performance evaluation; Product supply chains; Shared understanding; Theoretical boundary; Artificial intelligence; Benchmarking; Carbon; Climate change; Decision making; Decision support systems; Product design; Research; Supply chains","University of Sheffield","We gratefully acknowledge the financial support from Yorkshire Forward to support the Centre for Low Carbon Future (CLCF), which is a collaborative research centre between the Universities of Sheffield, York, Leeds and Hull. We wish to thank Muntons plc particularly Dr Nigel Davies (Manufacturing and Technical Director) for the case study; and the Business Advisory Board of the CLCF Low Carbon Supply Chain programme. We also wish to express our thanks to Shaping Cloud for their help in SCEnAT.",,,,,,,,,,"Lenny Koh, S.C.; Logistics and Supply Chain Management (LSCM) Research Centre, , Sheffield, United Kingdom; email: S.C.L.Koh@sheffield.ac.uk",,,00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-84873477519
"Liu S., Leat M., Moizer J., Megicks P., Kasturiratne D.","24174685600;6506929585;21935223600;24485585000;55209876300;","A decision-focused knowledge management framework to support collaborative decision making for lean supply chain management",2013,"International Journal of Production Research","51","7",,"2123","2137",,62,"10.1080/00207543.2012.709646","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873456162&doi=10.1080%2f00207543.2012.709646&partnerID=40&md5=a8ca9711cf35c7402a0541120ed07865","School of Management, University of Plymouth, Plymouth, Devon, United Kingdom","Liu, S., School of Management, University of Plymouth, Plymouth, Devon, United Kingdom; Leat, M., School of Management, University of Plymouth, Plymouth, Devon, United Kingdom; Moizer, J., School of Management, University of Plymouth, Plymouth, Devon, United Kingdom; Megicks, P., School of Management, University of Plymouth, Plymouth, Devon, United Kingdom; Kasturiratne, D., School of Management, University of Plymouth, Plymouth, Devon, United Kingdom","Lean supply chain management is a relatively new concept resulting from the integration of lean philosophy into supply chain management. Decision making in a lean supply chain context is challenging because of the complexity, dynamics, and uncertainty inherent to both supply networks and the types of waste (defined as any processes, including use of resources, which do not add value to customers). Efficient knowledge management has been identified as one of the key requirements to achieve integrated support for lean supply chain decisions. This paper proposes a decision-focused knowledge framework including a multi-layer knowledge model (to capture the know-why and know-with together with the know-what and know-how), a knowledge matrix for knowledge elicitation, and a decision tree for the design of the knowledge base. A knowledge system for lean supply chain management (KSLSCM) has been developed using artificial intelligence system shells VisiRule and Flex. The KSLSCM has five core components: a supply chain decision network manager, a waste elimination knowledge base, a knowledge refinement module, an inference engine, and a decision justifier. The knowledge framework and the KSLSCM have been evaluated through an industrial decision case. It has been demonstrated through the KSLSCM that the decision-focused knowledge framework can provide efficient and effective support for collaborative decision making in supply chain waste elimination. © 2013 Taylor & Francis Group, LLC.","collaborative decision making; decision-focused knowledge framework; Flex; knowledge-based decision support system; lean supply chain management; multi-layer knowledge model; VisiRule","Collaborative decision making; Flex; Knowledge based decision support systems; Knowledge frameworks; Knowledge model; VisiRule; Artificial intelligence; Decision support systems; Decision trees; Knowledge based systems; Knowledge management; Supply chain management; Technology transfer; Decision making",,,,,,,,,,,,"Liu, S.; School of Management, , Plymouth, Devon, United Kingdom; email: shaofeng.liu@plymouth.ac.uk",,,00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-84873456162
"Georgiadis P., Athanasiou E.","8384563500;6603107126;","Flexible long-term capacity planning in closed-loop supply chains with remanufacturing",2013,"European Journal of Operational Research","225","1",,"44","58",,79,"10.1016/j.ejor.2012.09.021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868483649&doi=10.1016%2fj.ejor.2012.09.021&partnerID=40&md5=96b54efec89cddc424714b7e47b944cc","Industrial Management Division, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Greece","Georgiadis, P., Industrial Management Division, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Greece; Athanasiou, E., Industrial Management Division, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Greece","We deal with long-term demand-driven capacity planning policies in the reverse channel of closed-loop supply chains (CLSCs) with remanufacturing, under high capacity acquisition cost coupled with uncertainty in actual demand, sales patterns, quality and timing of end-of-use product returns. The objective is to facilitate the decision-making when the management faces the dilemma of implementing either a strategy of early large-scale investments to benefit from economies of scale and capacity readiness, or a flexible strategy of low volume but more frequent capacity expansions. We consider a CLSC with two sequential product-types. We study the system's response in terms of transient flows, actual/desired capacity level, capacity expansions/contractions and total supply chain profit, employing a simulation-based system dynamics optimization approach. Extensive numerical investigation covers a broad range of real-world remanufacturable products under alternative scenarios in relation to the market preference over product-types. The key findings propose flexible policies as improved alternatives to large-scale capacity expansions/contractions in terms of adaptability to the actual pattern of end-of-use product returns and involved risk in the investments' turnover. Flexible policies are also proposed as practices to avoid overcapacity phenomena in collection and remanufacturing capacity and as robust policies to product demand. Their implementation is revealed to be even more important for the case of remanufacturing, when a high capacity acquisition unit-cost ratio (remanufacturing/collection) is coupled with strong economies of scale. Finally, results under different information sharing structures show changes in remanufacturing policies, thus justifying the importance of coordination between the decision-maker and the distributor. © 2012 Elsevier B.V. All rights reserved.","Capacity planning; Closed-loop supply chains; Decision support system; Supply chain management; System dynamics","Acquisition costs; Capacity expansion; Capacity planning; Closed-loop supply chain; Decision makers; Economies of scale; Flexible strategies; High capacity; Information sharing; Numerical investigations; Optimization approach; Over capacity; Product demand; Product returns; Remanufacturable products; Reverse channels; System Dynamics; Transient flow; Artificial intelligence; Decision support systems; Profitability; Supply chain management; System theory; Economics",,,,,,,,,,,,"Georgiadis, P.; Industrial Management Division, Greece; email: geopat@auth.gr",,,03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","",Scopus,2-s2.0-84868483649
"Latha Shankar B., Basavarajappa S., Chen J.C.H., Kadadevaramath R.S.","55260906700;12141008400;10243764200;26324982000;","Location and allocation decisions for multi-echelon supply chain network - A multi-objective evolutionary approach",2013,"Expert Systems with Applications","40","2",,"551","562",,119,"10.1016/j.eswa.2012.07.065","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867673023&doi=10.1016%2fj.eswa.2012.07.065&partnerID=40&md5=09937f844da946d2d399fd92adfa1b49","Dept. of Industrial Engg. and Management, SIT, Tumkur, India; Mechanical Department, UBDT, Davangere, India; Graduate School of Business, Gonzaga University, Spokane, WA, United States","Latha Shankar, B., Dept. of Industrial Engg. and Management, SIT, Tumkur, India; Basavarajappa, S., Mechanical Department, UBDT, Davangere, India; Chen, J.C.H., Graduate School of Business, Gonzaga University, Spokane, WA, United States; Kadadevaramath, R.S., Dept. of Industrial Engg. and Management, SIT, Tumkur, India","This paper aims at multi-objective optimization of single-product for four-echelon supply chain architecture consisting of suppliers, production plants, distribution centers (DCs) and customer zones (CZs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to DCs, from DCs to CZs so as to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met. To optimize these two objectives simultaneously, four-echelon network model is mathematically represented considering the associated constraints, capacity, production and shipment costs and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization (MOHPSO) algorithm. This evolutionary based algorithm incorporates non-dominated sorting algorithm into particle swarm optimization so as to allow this heuristic to optimize two objective functions simultaneously. This can be used as decision support system for location of facilities, allocation of demand points and monitoring of material flow for four-echelon supply chain network. © 2012 Elsevier Ltd. All rights reserved.","Evolutionary approach; Four-echelon supply chain architecture; MOHPSO; Non-dominated sorting algorithm; Swarm intelligence","Evolutionary approach; MOHPSO; Non-dominated Sorting; Supply chain architecture; Swarm Intelligence; Algorithms; Artificial intelligence; Decision support systems; Multiobjective optimization; Particle swarm optimization (PSO); Supply chains; Location",,,,,,,,,,,,"Chen, J.C.H.; Graduate School of Business, , Spokane, WA, United States; email: chen@jepson.gonzaga.edu",,,09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84867673023
"Moon I., Lee J.-H., Seong J.","7101610478;36104090700;55274370700;","Vehicle routing problem with time windows considering overtime and outsourcing vehicles",2012,"Expert Systems with Applications","39","18",,"13202","13213",,33,"10.1016/j.eswa.2012.05.081","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84865263865&doi=10.1016%2fj.eswa.2012.05.081&partnerID=40&md5=78ac9e6eb55173665a0d416622950cad","Department of Industrial Engineering, Pusan National University, Busan 609-735, South Korea; Postal and Logistics Technology Research Department, Electronics and Telecommunications Research Institute, Daejeon 305-700, South Korea; Overseas Business Planning Team, Sejong Industrial Co., Ltd., Ulsan 683-360, South Korea","Moon, I., Department of Industrial Engineering, Pusan National University, Busan 609-735, South Korea; Lee, J.-H., Postal and Logistics Technology Research Department, Electronics and Telecommunications Research Institute, Daejeon 305-700, South Korea; Seong, J., Overseas Business Planning Team, Sejong Industrial Co., Ltd., Ulsan 683-360, South Korea","The vehicle routing problem with time windows (VRPTW) is an important problem in third-party logistics and supply chain management. We extend the VRPTW to the VRPTW with overtime and outsourcing vehicles (VRPTWOV), which allows overtime for drivers and the possibility of using outsourced vehicles. This problem can be applied to third-party logistics companies for managing central distributor-local distributors, local distributor-retailers (or customers), and manufacturers. We developed a mixed integer programming model, a genetic algorithm (GA), and a hybrid algorithm based on simulated annealing. The computational results demonstrate the efficiency of the developed algorithms. We also develop a decision support system for the VRPTWOV that is equipped with a vehicle route rescheduling function for realistic situations based on the GA. © 2012 Elsevier Ltd. All rights reserved.","Decision support system; Genetic algorithm; Outsourcing vehicle; Simulated annealing; Vehicle routing","Computational results; Hybrid algorithms; Logistics and supply chain management; Logistics company; Mixed integer programming model; Vehicle routing problem with time windows; Artificial intelligence; Computer programming; Decision support systems; Genetic algorithms; Outsourcing; Simulated annealing; Supply chain management; Vehicle routing; Vehicles; Computational efficiency","Pusan National University, PNU","This work was supported by a 2-Year Research Grant of Pusan National University. This paper is an extended version of the preminary study published in the Journal of Korean Institute of Industrial Engineers in 2006.",,,,,,,,,,"Lee, J.-H.; Postal and Logistics Technology Research Department, , Daejeon 305-700, South Korea; email: jhunlee@etri.re.kr",,,09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84865263865
"Kadadevaramath R.S., Chen J.C.H., Latha Shankar B., Rameshkumar K.","26324982000;57019946600;55260906700;57190695664;","Application of particle swarm intelligence algorithms in supply chain network architecture optimization",2012,"Expert Systems with Applications","39","11",,"10160","10176",,60,"10.1016/j.eswa.2012.02.116","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862785546&doi=10.1016%2fj.eswa.2012.02.116&partnerID=40&md5=7264797f59d187b3293eaf838cfa558f","Industrial Engineering and Management, Siddaganga Institute of Technology, Tumkur 572103, Karnataka, India; Graduate School of Business, Gonzaga University, Spokane, WA, United States; Department of Mechanical Engineering, Amritha School of Engineering, Ettimadai, Coimbattore 641105, Tamil Nadu, India","Kadadevaramath, R.S., Industrial Engineering and Management, Siddaganga Institute of Technology, Tumkur 572103, Karnataka, India; Chen, J.C.H., Graduate School of Business, Gonzaga University, Spokane, WA, United States; Latha Shankar, B., Industrial Engineering and Management, Siddaganga Institute of Technology, Tumkur 572103, Karnataka, India; Rameshkumar, K., Department of Mechanical Engineering, Amritha School of Engineering, Ettimadai, Coimbattore 641105, Tamil Nadu, India","In today's globalization, the success of an industry is dependent on cost effective supply chain management under various markets, logistics and production uncertainties. Uncertainties in the supply chain usually decrease profit, i.e. increase total supply chain cost. Demand uncertainty and constraints posed by the every echelon are important factors to be considered in the supply chain design operations. Optimization is no longer a luxury but has become the order of the day. This paper specifically deals with the modeling and optimization of a three echelon supply chain network using the particle swarm optimization/intelligence algorithms. © 2012 Elsevier Ltd. All rights reserved.","Integration; Optimization; Particle swarm optimization/intelligence; Supply chain management","Architecture optimization; Cost effective; Demand uncertainty; Modeling and optimization; Particle swarm; Production uncertainty; Supply chain costs; Supply chain design; Supply chain network; Three-echelon; Algorithms; Artificial intelligence; Integration; Network architecture; Profitability; Supply chain management; Optimization",,,,,,,,,,,,"Kadadevaramath, R.S.; Industrial Engineering and Management, , Tumkur 572103, Karnataka, India; email: rajeshwarkmath@yahoo.com",,,09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84862785546
"Ferreira L., Borenstein D.","37006409400;7005024634;","A fuzzy-Bayesian model for supplier selection",2012,"Expert Systems with Applications","39","9",,"7834","7844",,70,"10.1016/j.eswa.2012.01.068","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858342275&doi=10.1016%2fj.eswa.2012.01.068&partnerID=40&md5=e2048959edc8115f84039df282ee832d","Science and Technology School, Federal University of Rio Grande Do Norte, Brazil; Management School, Federal University of Rio Grande Do sul, Brazil","Ferreira, L., Science and Technology School, Federal University of Rio Grande Do Norte, Brazil; Borenstein, D., Management School, Federal University of Rio Grande Do sul, Brazil","The selection supplier problem has received a lot of attention from academics in recent years. Several models were developed in the literature, combining consolidated operations research and artificial intelligence methods and techniques. However, the tools presented in the literature neglected learning and adaptation, since this decision making process is approached as a static one rather than a highly dynamic process. Delays, lack of capacity, quality related issues are common examples of dynamic aspects that have a direct impact on long-term relationships with suppliers. This paper presents a novel method based on the integration of influence diagram and fuzzy logic to rank and evaluate suppliers. The model was developed to support managers in exploring the strengths and weaknesses of each alternative, to assist the setting of priorities between conflicting criteria, to study the sensitivity of the behavior of alternatives to changes in underlying decision situations, and finally to identify a preferred course of action. To be effective, the computational implementation of the method was embedded into an information system that includes several functionalities such as supply chain simulation and supplier's databases. A case study in the biodiesel supply chain illustrates the effectiveness of the developed method. © 2012 Elsevier Ltd. All rights reserved.","Bayesian networks; Fuzzy; Influence diagrams; Supplier selection; Supply chain","Artificial intelligence methods; Computational implementations; Course of action; Decision making process; Decision situation; Direct impact; Dynamic aspects; Dynamic process; Fuzzy; Influence diagram; Learning and adaptation; Supplier selection; Supply chain simulation; Artificial intelligence; Expert systems; Fuzzy logic; Supply chains; Bayesian networks",,,,,,,,,,,,"Ferreira, L.; Science and Technology School, Brazil; email: ferreira@ufrnet.br",,,09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84858342275
"Hahn G.J., Kuhn H.","37006578000;7402259452;","Designing decision support systems for value-based management: A survey and an architecture",2012,"Decision Support Systems","53","3",,"591","598",,33,"10.1016/j.dss.2012.02.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862549719&doi=10.1016%2fj.dss.2012.02.016&partnerID=40&md5=31441ee02b1b838075cbf57c80cd73bf","Department of Supply Chain Management and Operations, Catholic University of Eichstaett, Ingolstadt, Germany","Hahn, G.J., Department of Supply Chain Management and Operations, Catholic University of Eichstaett, Ingolstadt, Germany; Kuhn, H., Department of Supply Chain Management and Operations, Catholic University of Eichstaett, Ingolstadt, Germany","Value-based Management (VBM) concepts are prevalent in theory and practice since shareholder value creation is commonly considered the paramount business goal. However, VBM mainly applies data-driven concepts to support decision-making, disregarding model-driven approaches. This paper develops a comprehensive approach to designing model-driven DSS for VBM. First, we derive a conceptual architecture for Integrated Business Planning (IBP) as the foundation for a model-driven approach to VBM. Second, we present a unified modeling approach for value-based performance and risk optimization that implements Value Added (xVA) performance metrics and applies robust optimization methods to mitigate risk impact. © 2012 Elsevier B.V. All rights reserved.","Financial management; Integrated business planning; Robust optimization; Supply chain management; Value-based management","Business goals; Conceptual architecture; Financial managements; Integrated business; Model driven approach; Model-driven; Performance metrics; Risk impact; Risk optimization; Robust optimization; Robust optimization method; Shareholder values; Theory and practice; Unified Modeling; Value-based; Artificial intelligence; Decision support systems; Shareholders; Strategic planning; Supply chain management; Optimization",,,,,,,,,,,,"Kuhn, H.Auf der Schanz 49, 85049 Ingolstadt, Germany; email: heinrich.kuhn@kuei.de",,,01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-84862549719
"Erdem A.S., Göen E.","57197017085;54896274900;","Development of a decision support system for supplier evaluation and order allocation",2012,"Expert Systems with Applications","39","5",,"4927","4937",,46,"10.1016/j.eswa.2011.10.024","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855879730&doi=10.1016%2fj.eswa.2011.10.024&partnerID=40&md5=3ba5cae866fa9b5ce3bcd9fd9fbe0651","Department of Management Information Systems, Bogazici University, 34342 Bebek, Istanbul, Turkey","Erdem, A.S., Department of Management Information Systems, Bogazici University, 34342 Bebek, Istanbul, Turkey; Göen, E., Department of Management Information Systems, Bogazici University, 34342 Bebek, Istanbul, Turkey","This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain. Supplier evaluation and order allocation are complex, multi criteria decisions. Initially, an analytic hierarchy process (AHP) model is developed for qualitative and quantitative evaluation of suppliers. Based on these evaluations, a goal programming (GP) model is developed for order allocation among suppliers. The models are integrated into a DSS that provides a dynamic, flexible and fast decision making environment. The DSS environment is tested at the purchasing department of a manufacturer and feedbacks are obtained. © 2011 Elsevier Ltd. All rights reserved.","Analytic hierarchy process; Decision support systems; Goal programming; Order allocation; Supplier evaluation","Decision supports; Goal programming; Multicriteria decision; Order allocation; Purchasing department; Quantitative evaluation; Supplier Evaluations; Analytic hierarchy process; Artificial intelligence; Decision support systems; Purchasing; Supply chains; Quality control",,,,,,,,,,,,"Erdem, A.S.; Department of Management Information Systems, , 34342 Bebek, Istanbul, Turkey; email: asli.erdem@boun.edu.tr",,,09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-84855879730
"Tako A.A., Robinson S.","26031798500;7402673952;","The application of discrete event simulation and system dynamics in the logistics and supply chain context",2012,"Decision Support Systems","52","4",,"802","815",,269,"10.1016/j.dss.2011.11.015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857653614&doi=10.1016%2fj.dss.2011.11.015&partnerID=40&md5=925a627dabd2dee0e534402ad72e64c4","School of Business and Economics, Loughborough University, Loughborough, LE11 3TU, United Kingdom","Tako, A.A., School of Business and Economics, Loughborough University, Loughborough, LE11 3TU, United Kingdom; Robinson, S., School of Business and Economics, Loughborough University, Loughborough, LE11 3TU, United Kingdom","Discrete event simulation (DES) and system dynamics (SD) are two modelling approaches widely used as decision support tools in logistics and supply chain management (LSCM). A widely held belief exists that SD is mostly used to model problems at a strategic level, whereas DES is used at an operational/tactical level. This paper explores the application of DES and SD as decision support systems (DSS) for LSCM by looking at the nature and level of issues modelled. Peer reviewed journal papers that use these modelling approaches to study supply chains, published between 1996 and 2006 are reviewed. A total of 127 journal articles are analysed to identify the frequency with which the two simulation approaches are used as modelling tools for DSS in LSCM. Our findings suggest that DES has been used more frequently to model supply chains, with the exception of the bullwhip effect, which is mostly modelled using SD. Based on the most commonly used modelling approach, issues in LSCM are categorised into four groups: the DES domain, the SD domain, the common domain and the less common domain. The study furthermore suggests that in terms of the level of decision making involved, strategic or operational/tactical, there is no difference in the use of either DES or SD. The results of this study inform the existing literature about the use of DES and SD as DSS tools in LSCM. © 2011 Elsevier B.V. All rights reserved.","Comparison of methods; Discrete-event simulation; Logistics and supply chain management; Simulation modelling; System dynamics","Bullwhip effects; Comparison of methods; Decision support tools; Discrete events; Four-group; Journal articles; Journal paper; Logistics and supply chain management; Model problems; Modelling tools; Simulation approach; Strategic level; System Dynamics; Artificial intelligence; Computer simulation; Decision support systems; Discrete event simulation; Supply chain management; System theory",,,,,,,,,,,,"Tako, A.A.; School of Business and Economics, , Loughborough, LE11 3TU, United Kingdom; email: a.takou@lboro.ac.uk",,,01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","All Open Access, Green",Scopus,2-s2.0-84857653614
"Kristianto Y., Gunasekaran A., Helo P., Sandhu M.","14625435900;56238759300;6506880314;12794696300;","A decision support system for integrating manufacturing and product design into the reconfiguration of the supply chain networks",2012,"Decision Support Systems","52","4",,"790","801",,72,"10.1016/j.dss.2011.11.014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857644010&doi=10.1016%2fj.dss.2011.11.014&partnerID=40&md5=bd265bd43fed730463aa80b38751b17a","Department of Production, University of Vaasa, P.O. Box 700, FI-65101 Vaasa, Finland; Department of Decision and Information Sciences, Charlton College of Business, University of Massachusetts - Dartmouth, 285 Old Westport Road, North Dartmouth, MA 02747-2300, United States; Faculty of Business and Economics, United Arab Emirates University, P.O. Box 17555, Al-Ain, United Arab Emirates","Kristianto, Y., Department of Production, University of Vaasa, P.O. Box 700, FI-65101 Vaasa, Finland; Gunasekaran, A., Department of Decision and Information Sciences, Charlton College of Business, University of Massachusetts - Dartmouth, 285 Old Westport Road, North Dartmouth, MA 02747-2300, United States; Helo, P., Department of Production, University of Vaasa, P.O. Box 700, FI-65101 Vaasa, Finland; Sandhu, M., Faculty of Business and Economics, United Arab Emirates University, P.O. Box 17555, Al-Ain, United Arab Emirates","A supply chain needs to meet its customers' requirements (CRs) in terms of delivery lead times, total costs and product quality. The objective of this article is to improve the level of integration in all aspects of supply chain reconfiguration, such as the inventory allocation and manufacturing process involved, by incorporating manufacturing and product design into logistic design. The effect of uncertain customer demand, production and supply lead times are studied. An optimum supply chain network is configured by combining optimization at the strategic and tactical level. A system dynamic based computer simulation model is used to validate the operations of the supply chain. The performance of the system is measured in terms of backorders and inventory level. The results and analysis indicate that fewer stockholding points and a shorter review period of demand can improve performance in this respect. In addition, a proposal for improving the performance of supply chain in terms of lower safety stocks is presented. Finally, management decision-making is discussed, among other concluding remarks. © 2011 Elsevier B.V. All rights reserved.","Assembly; Decision Support Systems; Inventory allocation; Product design; Supply chain","Backorders; Computer simulation model; Customer demands; Decision supports; Inventory allocation; Inventory levels; Lead time; Management decision-making; Manufacturing process; Product quality; Safety stock; Supply chain network; System Dynamics; Total costs; Artificial intelligence; Assembly; Computer simulation; Decision support systems; Manufacture; Product design; Supply chains",,,,,,,,,,,,"Kristianto, Y.; Department of Production, P.O. Box 700, FI-65101 Vaasa, Finland; email: ykristiantonugroho@gmail.com",,,01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-84857644010
"Okongwu U., Lauras M., Dupont L., Humez V.","24074690600;19638996200;7102011095;55256353800;","A decision support system for optimising the order fulfilment process",2012,"Production Planning and Control","23","8",,"581","598",,16,"10.1080/09537287.2011.566230","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862568502&doi=10.1080%2f09537287.2011.566230&partnerID=40&md5=47c83c5967dd2c82515a38d7b4dac545","Department of Industrial Organisation, Logistics and Technology, Université de Toulouse, Toulouse Business School, 20 Boulevard Lascrosses, 31068 Toulouse Cedex 7, France; Department of Industrial Engineering, Université de Toulouse, Mines Albi, Route de Teillet, 81013 Albi Cedex 9, France","Okongwu, U., Department of Industrial Organisation, Logistics and Technology, Université de Toulouse, Toulouse Business School, 20 Boulevard Lascrosses, 31068 Toulouse Cedex 7, France; Lauras, M., Department of Industrial Organisation, Logistics and Technology, Université de Toulouse, Toulouse Business School, 20 Boulevard Lascrosses, 31068 Toulouse Cedex 7, France, Department of Industrial Engineering, Université de Toulouse, Mines Albi, Route de Teillet, 81013 Albi Cedex 9, France; Dupont, L., Department of Industrial Engineering, Université de Toulouse, Mines Albi, Route de Teillet, 81013 Albi Cedex 9, France; Humez, V., Department of Industrial Engineering, Université de Toulouse, Mines Albi, Route de Teillet, 81013 Albi Cedex 9, France","Many authors have highlighted gaps at the interfaces between supply chains (SCs) and demand chains. Generally, the latter tends primarily to be 'agile' by maximising effectiveness and responsiveness while the former tends to be lean by maximising efficiency. When, in the SC, disruptions (that lead to stock-out situations) occur after customer orders have been accepted, managers are faced with the problem of maximising customer satisfaction while taking into consideration the conflicting objectives of the supply and demand sides of the order fulfilment process. This article proposes a cross-functional multi-criteria decision-making (advanced available-to-promise) tool that provides different strategic options from which a solution can be chosen. It also proposes a performance measurement system to support the decision-making and improvement process. The results of some experimental tests show that the model enables to make strategic decisions on the degree of flexibility required to achieve the desired level of customer service. © 2012 Taylor & Francis.","advanced available-to-promise; agility; demand chain; lean; order fulfilment process; supply chain","agility; Available-to-promise; Demand chains; lean; order fulfilment process; Artificial intelligence; Decision making; Decision support systems; Economics; Supply chains; Customer satisfaction",,,,,,,,,,,,"Lauras, M.; Department of Industrial Organisation, Logistics and Technology, 20 Boulevard Lascrosses, 31068 Toulouse Cedex 7, France; email: matthieu.lauras@mines-albi.fr",,,09537287,,PPCOE,,"English","Prod Plann Control",Article,"Final","All Open Access, Green",Scopus,2-s2.0-84862568502
"Khataie A.H., Bulgak A.A., Segovia J.J.","35146047500;6603338561;35146783300;","Activity-Based Costing and Management applied in a hybrid Decision Support System for order management",2011,"Decision Support Systems","52","1",,"142","156",,21,"10.1016/j.dss.2011.06.003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-80455155128&doi=10.1016%2fj.dss.2011.06.003&partnerID=40&md5=bd206c2b5e95687a4b5c408565b2c992","Department of Mechanical and Industrial Engineering, Concordia University, 1515 St. Catherine St. West, Montréal QC, H3G 1M8, Canada; John Molson School of Business, Concordia University, Montreal, 1450 Guy Street, Montréal QC H3G 1M8, Canada","Khataie, A.H., Department of Mechanical and Industrial Engineering, Concordia University, 1515 St. Catherine St. West, Montréal QC, H3G 1M8, Canada; Bulgak, A.A., Department of Mechanical and Industrial Engineering, Concordia University, 1515 St. Catherine St. West, Montréal QC, H3G 1M8, Canada; Segovia, J.J., John Molson School of Business, Concordia University, Montreal, 1450 Guy Street, Montréal QC H3G 1M8, Canada","This article introduces a new Cost Management and Decision Support System (DSS) applicable to Order Management. This model is better fit and compatible with today's competitive, and constantly changing, business environment. The presented Profitable-To-Promise (PTP) approach is a novel modeling approach which integrates System Dynamics (SD) simulation with Mixed-Integer Programming (MIP). This Order Management model incorporates Activity-Based Costing and Management (ABC/M) as a link to merge the two models, MIP and SD. This combination is introduced as a hybrid Decision Support System. Such a system can evaluate the profitability of each Order Fulfillment policy and generate valuable cost information. Unlike existing optimization-based DSS models, the presented hybrid modeling approach can perform on-time cost analysis. This will lead to better business decisions based on the updated information. © 2011 Elsevier B.V. All rights reserved.","Activity-Based Costing and Management; Cost control; Decision Support System; Mixed-Integer Programming; Supply chain management; System Dynamics","Activity-Based Costing and Management; Cost control; Decision supports; Mixed-Integer Programming; System Dynamics; Artificial intelligence; Cost accounting; Cost benefit analysis; Costs; Decision support systems; Integer programming; Profitability; Supply chain management; System theory; Decision making",,,,,,,,,,,,"Khataie, A.H.; Department of Mechanical and Industrial Engineering, 1515 St. Catherine St. West, Montréal QC, H3G 1M8, Canada; email: a_khatai@encs.concordia.ca",,,01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-80455155128
"Marchetta M.G., Mayer F., Forradellas R.Q.","15623824500;10339279900;15622890300;","A reference framework following a proactive approach for Product Lifecycle Management",2011,"Computers in Industry","62","7",,"672","683",,47,"10.1016/j.compind.2011.04.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960898395&doi=10.1016%2fj.compind.2011.04.004&partnerID=40&md5=0615619e08b61da0a772076de4afa998","School of Engineering, National University of Cuyo, Centro Universitario, CC405 (M5500AAT) Mendoza, Argentina; CONICET, Argentina; Research Team in Innovative Processes (EA N3767 ERPI), ENSGSI, Institut National Polytechnique de Lorraine, 8 rue Bastien Lepage, 54010 Nancy Cedex, France","Marchetta, M.G., School of Engineering, National University of Cuyo, Centro Universitario, CC405 (M5500AAT) Mendoza, Argentina, CONICET, Argentina; Mayer, F., Research Team in Innovative Processes (EA N3767 ERPI), ENSGSI, Institut National Polytechnique de Lorraine, 8 rue Bastien Lepage, 54010 Nancy Cedex, France; Forradellas, R.Q., School of Engineering, National University of Cuyo, Centro Universitario, CC405 (M5500AAT) Mendoza, Argentina","Product Lifecycle Management (PLM) has been identified as a key concept within manufacturing industries for improving product quality, time-to-market and costs. Previous works on this field are focused on processes, functions and information models, and those aimed at putting more intelligence on products are related to specific parts of the product lifecycle (e.g. supply chain management, shop floor control). Therefore, there is a lack of a holistic approach to PLM, putting more intelligence on products through the complete lifecycle. In this paper, a PLM framework supported by a proactive approach based on intelligent agents is proposed. The developed model aims at being a first step toward a reference framework for PLM, and complements past works on both product information and business process models (BPM), by putting proactivity on product's behavior. An example of an instantiation of the reference framework is presented as a case study. © 2011 Elsevier B.V. All rights reserved.","Concurrent Engineering; Intelligent Agent; PLM; Proactive Product; Virtual Enterprise","Business process model; Developed model; Holistic approach; Information models; Manufacturing industries; PLM; Pro-active approach; Proactive Product; Proactivity; Product information; Product life cycle management; Product quality; Product-life-cycle; Shop floor Control; Time-to-market; Virtual enterprise; Artificial intelligence; Concurrent engineering; Information theory; Intelligent agents; Supply chain management; Virtual corporation; Life cycle","ARF-08-04; F-599; Consejo Nacional de Investigaciones Científicas y Técnicas, CONICET; Universidad Nacional de Cuyo, UNCUYO","This research was jointly developed by the School of Engineering (UNCuyo, Argentina) , National Council on Scientific and Technical Research (CONICET, Argentina) and Research Team in Innovative Processes (EA N3767 ERPI, INPL, France) , and partially financed by the PREMER F-599, ARFITEC ARF-08-04 programs, and CONICET PhD fellowship.",,,,,,,,,,"Marchetta, M.G.; School of Engineering, , CC405 (M5500AAT) Mendoza, Argentina; email: mmarchetta@fing.uncu.edu.ar",,,01663615,,CINUD,,"English","Comput Ind",Article,"Final","All Open Access, Green",Scopus,2-s2.0-79960898395
"Kumar V., Mishra N., Chan F.T.S., Verma A.","36835614000;24167016200;7202586517;35197325900;","Managing warehousing in an agile supply chain environment: An F-AIS algorithm based approach",2011,"International Journal of Production Research","49","21",,"6407","6426",,18,"10.1080/00207543.2010.528057","https://www.scopus.com/inward/record.uri?eid=2-s2.0-80053008148&doi=10.1080%2f00207543.2010.528057&partnerID=40&md5=9361b36b58ef77bd7041f08e971913d6","Department of Management and Information Systems, Dublin City University Business School, Dublin, Ireland; School of Computer Science and Information Technology, University of Nottingham, Nottingham, United Kingdom; Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hom, Hong Kong; Computer Aided Manufacturing Laboratory, Department of Mechanical Engineering, University of Cincinnati, United States; School of Management and Business, Aberystwyth University, United Kingdom","Kumar, V., Department of Management and Information Systems, Dublin City University Business School, Dublin, Ireland; Mishra, N., School of Computer Science and Information Technology, University of Nottingham, Nottingham, United Kingdom, School of Management and Business, Aberystwyth University, United Kingdom; Chan, F.T.S., Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hom, Hong Kong; Verma, A., Computer Aided Manufacturing Laboratory, Department of Mechanical Engineering, University of Cincinnati, United States","In recent years, application of the agile concept in the manufacturing sector has been researched extensively to reduce the varying effect of customer demands. However, most of the research work is focused on the shop floor of different manufacturing processes, while issues concerning the control of warehouse scheduling in a supply chain have been neglected so far. Realising this in the present research an attempt has been made to address the scheduling aspect of a warehouse in an agile supply chain environment. To resolve the warehouse problem in this paper, the authors have proposed a new Fuzzy incorporated Artificial Immune System Algorithm (F-AIS). This algorithm encapsulates the salient features of a fuzzy logic controller and immune system. The proposed algorithm has been compared with genetic algorithm (GA), simulated annealing (SA) and artificial immune system (AIS) algorithm to reveal the efficacy of the proposed F-AIS algorithm. © 2011 Taylor & Francis.","agile; AIS; fuzzy; scheduling; supply chain; warehousing","Artificial intelligence; Fuzzy logic; Genetic algorithms; Immune system; Industrial research; Manufacture; Scheduling; Simulated annealing; Supply chains; Warehouses; agile; Artificial immune system algorithms; fuzzy; Fuzzy logic controllers; Manufacturing process; Manufacturing sector; Warehouse scheduling; warehousing; Agile manufacturing systems",,,,,,,,,,,,"Chan, F.T.S.; Department of Industrial and Systems Engineering, , Hung Hom, Hong Kong; email: f.chan@inet.polyu.edu.hk",,"Taylor and Francis Ltd.",00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-80053008148
"Ghandforoush P., Sen T.K.","6601984712;36003811200;","A DSS to manage platelet production supply chain for regional blood centers",2010,"Decision Support Systems","50","1",,"32","42",,84,"10.1016/j.dss.2010.06.005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049453036&doi=10.1016%2fj.dss.2010.06.005&partnerID=40&md5=cf04c311ca536de54c7f6ae263c80bf6","Business Information Technology, Virginia Polytechnic Institute, State University, 7054 Haycock Road, Falls Church, VA 22043, United States; Accounting and Information Systems, Pamplin College of Business, Virginia Tech, 7054 Haycock Road, Falls Church, VA 22043, United States","Ghandforoush, P., Business Information Technology, Virginia Polytechnic Institute, State University, 7054 Haycock Road, Falls Church, VA 22043, United States; Sen, T.K., Accounting and Information Systems, Pamplin College of Business, Virginia Tech, 7054 Haycock Road, Falls Church, VA 22043, United States","This paper presents a prototype decision support system for platelet production and blood mobile scheduling for a regional blood center. Unlike whole blood cells, platelets have a very short shelf life, which requires matching demand and supply closely. This is achieved by an efficient supply chain DSS that is optimized for delivery of platelets from production centers to transfusion centers, typically hospitals. One of the critical elements of the DSS is an embedded non-convex integer optimization model that assists the regional blood center manager to schedule the shuttle transportation of whole blood from collection sites to the regional processing center. The proposed non-convex integer model is transformed to a linear 0-1 problem using a two-step conversion process. The transformed model is successfully solved and the optimal solution is reached for the test data. An application of the integrated DSS using data from a regional blood center is described. The results suggest that the proposed DSS better meets the daily demand by producing a superior production plan and mobile assignment schedule. © 2010 Elsevier B.V. All rights reserved.","Blood bank DSS; DSS; Integer nonlinear programming; Platelet supply management","Blood bank; Conversion process; Critical elements; Demand and supply; DSS; Integer optimization; Integer-nonlinear programming; Optimal solutions; Production plans; Regional processing centers; Shelf life; Test data; Whole blood; Artificial intelligence; Decision support systems; Decision theory; Integer programming; Nonlinear programming; Optimization; Platelets; Production control; Supply chain management; Supply chains; Blood",,,,,,,,,,,,"Ghandforoush, P.; Business Information Technology, 7054 Haycock Road, Falls Church, VA 22043, United States; email: pghandfo@vt.edu",,,01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-78049453036
"Warren Liao T., Chang P.C.","7102718917;56122892000;","Impacts of forecast, inventory policy, and lead time on supply chain inventoryA numerical study",2010,"International Journal of Production Economics","128","2",,"527","537",,41,"10.1016/j.ijpe.2010.07.002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049275274&doi=10.1016%2fj.ijpe.2010.07.002&partnerID=40&md5=527e5c2d9de594b8aef1bbc01ed59e90","Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, United States; Department of Information Management, Yuan Ze University, Chung-Li 32003, Taiwan","Warren Liao, T., Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, United States; Chang, P.C., Department of Information Management, Yuan Ze University, Chung-Li 32003, Taiwan","This paper first proposes the use of metaheuristic, to combine with exponential smoothing methods, in forecasting future demands and in determining the optimal inventory policy values for each node in a supply chain network based on historical demand or order streams without the need of any prior knowledge about the demand distribution or distribution fitting. The effects of five demand forecasting methods, two inventory policies, and three lead times on the total inventory cost of a 3-echelon serial supply chain system are then investigated. The effect of sharing the demand information for planning the inventories is also compared with that of no sharing. For testing, 15 quarterly and 15 monthly time series were taken from the M3 Competition and are considered as the multi-item demand streams to be fulfilled in the supply chain. The results indicate that: (1) the damped Pegel forecasting method is the best in terms of prediction errors because it outperforms others in three of five measures, followed by the simple exponential smoothing that wins one of the remaining two and ties the damped Pegel in one; (2) the supply chain inventory cost increases with increasing lead time and echelon level of the supply chain when the (s, S) policy is used, but not the (r, Q) policy; (3) the (r, Q) inventory policy generally incurs lower supply chain inventory cost than the (s, S) policy; (4) sharing demand information reduces inventory cost and the reduction is higher for (s, S) than for (r, Q); (5) the best demand forecasting method for minimizing inventory cost varies with the inventory policy used and lead time; and (6) the correlation between forecasting errors and inventory costs is either negligible or minimal. © 2010 Elsevier B.V. All rights reserved.","Ant colony optimization; Demand forecasting; Information sharing; Inventory cost; Inventory policy; Lead time; Metaheuristic; Supply chain","Ant-colony optimization; Demand forecasting; Information sharing; Inventory costs; Inventory policies; Lead time; Metaheuristic; Algorithms; Artificial intelligence; Chains; Competition; Costs; Errors; Forecasting; Heuristic methods; Information dissemination; Information management; Information retrieval; Inventory control; Optimization; Supply chain management; Time series; Time series analysis; Supply chains",,,,,,,,,,,,"Warren Liao, T.; Department of Mechanical and Industrial Engineering, , Baton Rouge, LA 70803, United States; email: ieliao@lsu.edu",,,09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-78049275274
"Li Y., Kramer M.R., Beulens A.J.M., Van Der Vorst J.G.A.J.","36910837800;7402838267;6603257562;6506505018;","A framework for early warning and proactive control systems in food supply chain networks",2010,"Computers in Industry","61","9",,"852","862",,36,"10.1016/j.compind.2010.07.010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049242483&doi=10.1016%2fj.compind.2010.07.010&partnerID=40&md5=ace9a19542c1b04c91e792f56eb1e503","Information Technology Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, Netherlands; Operations Research and Logistics Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, Netherlands","Li, Y., Information Technology Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, Netherlands; Kramer, M.R., Information Technology Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, Netherlands; Beulens, A.J.M., Information Technology Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, Netherlands; Van Der Vorst, J.G.A.J., Operations Research and Logistics Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, Netherlands","It is inherent to food supply chain networks that performance deviations occur occasionally due to variations in product quality and quantity. To reduce losses, one wants to be informed about such deviations as soon as possible, preferably even before they occur. Then it is possible to take actions to prevent or reduce negative consequences. In practice, extensive operational data is recorded, forming a valuable source for early warning and proactive control systems, i.e. decision support systems for prediction and prevention of such performance problems. Data mining methods are ideal for analyzing such data sources and extracting useable information from them. In this paper, we present a novel framework for early warning and proactive control systems that combine expert knowledge and data mining methods to exploit recorded data. We discuss the implementation of a prototype system and the experiences from a case study regarding the applicability of the framework. © 2010 Elsevier B.V. All rights reserved.","Data mining; Early warning; Knowledge management; Proactive control","Data mining methods; Data source; Early warning; Expert knowledge; Operational data; Performance problems; Proactive control; Product quality; Prototype system; Agriculture; Artificial intelligence; Control theory; Decision support systems; Decision theory; Food supply; Knowledge management; Supply chain management; Supply chains; Data mining",,,,,,,,,,,,"Kramer, M. R.; Information Technology Group, Hollandseweg 1, 6706 KN Wageningen, Netherlands; email: Mark.Kramer@wur.nl",,,01663615,,CINUD,,"English","Comput Ind",Article,"Final","",Scopus,2-s2.0-78049242483
"Zhou L., Disney S., Towill D.R.","57204687859;55931127600;7007047559;","A pragmatic approach to the design of bullwhip controllers",2010,"International Journal of Production Economics","128","2",,"556","568",,29,"10.1016/j.ijpe.2010.07.023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049239890&doi=10.1016%2fj.ijpe.2010.07.023&partnerID=40&md5=9bfffafd8edd8fa5736ad0302c97046f","Systems Management and Strategy Department, Greenwich Business School, London SE10 9LS, United Kingdom; Logistics Systems Dynamics Group, Cardiff Business School, Cardiff, CF10 3EU, United Kingdom","Zhou, L., Systems Management and Strategy Department, Greenwich Business School, London SE10 9LS, United Kingdom; Disney, S., Logistics Systems Dynamics Group, Cardiff Business School, Cardiff, CF10 3EU, United Kingdom; Towill, D.R., Logistics Systems Dynamics Group, Cardiff Business School, Cardiff, CF10 3EU, United Kingdom","It is well known that forecasting mechanisms can greatly increase ""bullwhip"" demand variance amplification of orders as processed by both human and algorithmic decision makers. This paper is concerned with the application of the well-established APIOBPCS Decision Support System (a variant of the Order-Up-To Rule) in such circumstances. It has two feedback controls (based on the inventory and the orders-in-pipeline respectively) with gains set equal according to the DezielEilon Rule. There is one feed-forward control based on exponential forecasting, although this is not a restriction on the application of this system. We consider the pragmatic role of APIOBPCS in the situation where the echelon decision maker may be handling a wide range of SKU's in a non-altruistic environment where upmarket information may either be withheld or simply unavailable. Under such circumstances it has been established via site-based studies that the decision makers output (the orders) reflect a wide range of strategies (or maybe ignorance). Three strategies may be regarded as ""appropriate"", i.e. Pass-orders-Along; Demand Smoothing; and Level Scheduling depending, on context. APIOBPCS can be adapted to each of these modus operandi. In the first case with the added capability of smoothing the ""sharp edges"" with a modicum of inventory variation, and in the last case with the advantage of built-in trend detection. ""Players"" in non-altruistic supply chains must be able to cope with added uncertainties due to lead-time variations. We show that APIOBPCS may be well matched to such situations and is hence ""copable"" as well as ""capable"". The paper includes recommended parameter settings according to desired decision-making policies. © 2010 Elsevier B.V. All rights reserved.","APIOBPCS; Bullwhip; Control theory; Forecasting; System dynamics","Added uncertainty; APIOBPCS; Bullwhip; Decision makers; Leadtime; Modus operandi; Parameter setting; Sharp edges; System Dynamics; Trend detection; Artificial intelligence; Control theory; Decision support systems; Decision theory; Forecasting; Supply chains; System theory; Decision making","University of Greenwich","Dr. Li Zhou would like to thank the University of Greenwich for sponsoring a ten-month secondment to the Cardiff Business School under the competitive RAE funding award.",,,,,,,,,,"Zhou, L.; Systems Management and Strategy Department, , London SE10 9LS, United Kingdom; email: zl14@gre.ac.uk",,,09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-78049239890
"Fröhling M., Schwaderer F., Bartusch H., Rentz O.","23766956500;35241478800;35239528800;7006350365;","Integrated planning of transportation and recycling for multiple plants based on process simulation",2010,"European Journal of Operational Research","207","2",,"958","970",,26,"10.1016/j.ejor.2010.04.031","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955549358&doi=10.1016%2fj.ejor.2010.04.031&partnerID=40&md5=5398b8fa1fc74f540aecb98226c730bd","Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), Hertzstraße 16, D-76187 Karlsruhe, Germany","Fröhling, M., Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), Hertzstraße 16, D-76187 Karlsruhe, Germany; Schwaderer, F., Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), Hertzstraße 16, D-76187 Karlsruhe, Germany; Bartusch, H., Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), Hertzstraße 16, D-76187 Karlsruhe, Germany; Rentz, O., Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), Hertzstraße 16, D-76187 Karlsruhe, Germany","By-products accrue in all stages of industrial production networks. Legal requirements, shortening of primary resources and their increasing prices make their recycling more and more important. For the re-integration into the economic cycle the scope of common supply chain management is enlarged and so-called closed-loop supply chains with adapted and new planning tasks are developed. In process industries this requires a detailed modelling of the recycling processes. This is of special relevance for operational planning tasks in which an optimal usage of a given production system is envisaged. This contribution presents an integrated planning approach for a real-world case study from the zinc industry to achieve such an adequate process modelling. We consider the planning problem of a company that operates four metallurgical recycling plants and has to allocate residues from different sources to these recycling sites. The allocation determines the raw material mix used in the plants. This blending has an effect on the transportation costs and the costs and revenues of the individual technical processes in the recycling plants. Therefore in this problem transportation and recycling planning for multiple sites have to be regarded in an integrated way. The necessary detailed process modelling is achieved by the use of a flowsheet process simulation system to model each recycling plant individually. The models are used to derive linear input-output functions by multiple linear regression analyses. These are used in an integrated planning model to calculate the decision-relevant input and output flows that are dependent upon the allocation of the residues to the recycling sites. The model is embedded in a decision support system for the operational use. An example application and sensitivity analyses demonstrate and validate the approach and its potentials. The approach is transferable to other recycling processes as well as to other processes in process industries. © 2010 Elsevier B.V. All rights reserved.","Blending; Closed-loop supply chains; Integrated planning; Operational planning; Process industries; Process modelling","Closed-loop supply chain; Integrated planning; Operational planning; Process industries; Process modelling; Artificial intelligence; Blending; Computer simulation; Decision support systems; Decision theory; Economics; Industry; Linear regression; Planning; Raw materials; Recycling; Sensitivity analysis; Supply chain management; Transportation; Zinc; Supply chains",,,,,,,,,,,,"Fröhling, M.; Karlsruhe Institute of Technology (KIT), Hertzstraße 16, D-76187 Karlsruhe, Germany; email: magnus.froehling@kit.edu",,,03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","",Scopus,2-s2.0-77955549358
"Di Giacomo L., Patrizi G.","13410622700;56653707500;","Methodological analysis of supply chains management applications",2010,"European Journal of Operational Research","207","1",,"249","257",,9,"10.1016/j.ejor.2010.05.003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953870415&doi=10.1016%2fj.ejor.2010.05.003&partnerID=40&md5=d381d4640882e418d62c61f15cb80b02","Dipartimento di Statistica, Probabilità e Statistiche Applicate Università di Roma La Sapienza, Italy","Di Giacomo, L., Dipartimento di Statistica, Probabilità e Statistiche Applicate Università di Roma La Sapienza, Italy; Patrizi, G., Dipartimento di Statistica, Probabilità e Statistiche Applicate Università di Roma La Sapienza, Italy","Formal modelling may be used to express management operational plans to achieve the desired normative objectives of firms. The plans so formulated should be demonstrably optimal with regard to certain specific objectives assumed by top management and ought to provide accurate results, when enacted, with a given tolerance at a prespecified probability. Modelling Decision Support Systems is based on various alternative methodologies: managerial-situational, interpretative or formal-deductive, which affect the results and precision obtainable. The third approach requires dynamical nonlinear stochastic modelling to determine precise Supply Chain Management (SCM) plans, without incurring in the limitations that may characterize the former approaches. The aim of this paper is to examine different management methodologies, to determine the most appropriate implementation for accurate SCM plans. Two well known SCM implementations, the bullwhip effect and the collaborative planning and extensions will be examined under different methodologies for clarity and to verify their limitations. © 2010 Elsevier B.V. All rights reserved.","Decision support systems; Modelling systems and languages; Set-valued mappings; Supply chain management; System dynamics","Bullwhip effects; Collaborative planning; Decision supports; Formal modelling; Management applications; Operational plan; Set-valued mapping; Stochastic modelling; Supply chain management system; Top management; Artificial intelligence; Decision making; Decision support systems; Decision theory; Linguistics; Supply chains; System theory; Supply chain management",,,,,,,,,,,,"Patrizi, G.; Dipartimento di Statistica, Italy; email: patrizi@banach.sta.uniroma1.it",,,03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","",Scopus,2-s2.0-77953870415
"Qu T., Huang G.Q., Zhang Y., Dai Q.Y.","35590322600;7403425048;8305738300;7202735140;","A generic analytical target cascading optimization system for decentralized supply chain configuration over supply chain grid",2010,"International Journal of Production Economics","127","2",,"262","277",,28,"10.1016/j.ijpe.2009.08.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956058829&doi=10.1016%2fj.ijpe.2009.08.008&partnerID=40&md5=f92aed9024a7bb8c6d02cf1ff410fe49","Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong; State Key Laboratory for Manufacturing Systems Engineering, Xi'An Jiaotong University, Xi'an, China; Department of Electromechanical Engineering, Guangdong University of Technology, China","Qu, T., Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong; Huang, G.Q., Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong; Zhang, Y., Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong, State Key Laboratory for Manufacturing Systems Engineering, Xi'An Jiaotong University, Xi'an, China; Dai, Q.Y., Department of Electromechanical Engineering, Guangdong University of Technology, China","While centralized supply chain configuration (SCC) adopts an integrated decision model solved by an all-in-one decision method, decentralized SCC normally allows constituent enterprises to employ distributed decision models which are coordinated through a decomposition method to achieve an overall solution. Decentralized SCC paradigm could offer various contemporary advantages such as individual suppliers' decision right protection and overall decision efficiency enhancement. This paper proposes an optimization system, atcPortal, to practically enable such a decentralized SCC process. Individual suppliers convert their local decision support systems into decision web services to form a distributed open-standard SCC service platform, called supply chain grid (SCG) in this paper. As a decomposition-based optimization method, analytical target cascading (ATC) is the mechanism for atcPortal to coordinate these web services through three phases of service searching, service-based ATC problem definition, and service-oriented ATC execution. atcPortal is a generic and extensible web portal in the sense that ATC accommodates a variety of decentralized SCC decision structures without confining the local decision models of individual enterprises. Finally, the usage of atcPortal is demonstrated through a typical decentralized SCC problem. © 2009 Elsevier B.V.All rights reserved.","Analytical target cascading; Decentralized decision making; Supply chain configuration; Web service","Analytical target cascading; Decentralized decision making; Decentralized supply chains; Decision method; Decision right; Decomposition methods; Decomposition-based optimization; Distributed decision; Efficiency enhancement; Integrated decision; Local decisions; Optimization system; Problem definition; Service Oriented; Service platforms; Service-based; Supply chain configuration; Three phasis; Web portal; Artificial intelligence; Decision making; Decision support systems; Decision theory; Models; Optimization; Security of data; Supply chain management; Web services; Supply chains","University Research Committee, University of Hong Kong, URC, HKU: GHP/042/07LP","This work has been partially supported by HKU Research Committee, Hong Kong SAR RGC and ITF (GHP/042/07LP). The authors also want to acknowledge 2006-863 RFID special-topic project (2006AA04A124) and 2006 Guangdong Department of Education's major project (cgzhzd0608).",,,,,,,,,,"Qu, T.; Department of Industrial and Manufacturing Systems Engineering, Pokfulam Road, Hong Kong, Hong Kong; email: quting@hku.hk",,,09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-77956058829
"Georgiadis P., Athanasiou E.","8384563500;6603107126;","The impact of two-product joint lifecycles on capacity planning of remanufacturing networks",2010,"European Journal of Operational Research","202","2",,"420","433",,63,"10.1016/j.ejor.2009.05.022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349921094&doi=10.1016%2fj.ejor.2009.05.022&partnerID=40&md5=92115b9636dd55802ff4fedae4f53729","Industrial Management Division, Department of Mechanical Engineering, Aristotle University of Thessaloniki, P.O. Box 461, 541 24 Thessaloniki, Greece","Georgiadis, P., Industrial Management Division, Department of Mechanical Engineering, Aristotle University of Thessaloniki, P.O. Box 461, 541 24 Thessaloniki, Greece; Athanasiou, E., Industrial Management Division, Department of Mechanical Engineering, Aristotle University of Thessaloniki, P.O. Box 461, 541 24 Thessaloniki, Greece","Capacity planning in the reverse channel of closed-loop supply chains (CLSCs) involves complex issues due to the different lifecycles of product offerings in combination with the variability regarding product usage time, quality level of used products and return patterns. (Georgiadis, P., Vlachos, D., Tagaras, G., 2006. The impact of product lifecycle on capacity planning of closed-loop supply chains with remanufacturing. Production and Operations Management 15; 514-527) developed a system dynamics (SD) model to study a CLSC with remanufacturing for a single product which incorporates a dynamic capacity modeling approach. We extend this SD model for two sequential product-types under two alternative scenarios regarding the market preferences over the product-types; in the first scenario, the market is considered showing no preferences, while in the second scenario, the demand over a product-type can be satisfied only by providing units of the specific type. We study how the joint lifecycles of two product-types, entry time of the second product-type to the market and used product return patterns affect the optimal policies regarding expansion and contraction of collection and remanufacturing capacities. The results of extensive numerical investigation are tested for their statistical significance using analysis of variance (ANOVA). In the first scenario, the results show that the system performs best when the two lifecycles form a trapezoid pattern for total demand while in the second scenario, when the two lifecycles form a triangular pattern. © 2009 Elsevier B.V. All rights reserved.","Capacity planning; Closed-loop supply chain; Decision support system; Robustness and sensitivity analysis; System dynamics","Capacity planning; Closed-loop supply chain; Dynamic capacity; Expansion and contraction; Numerical investigations; Optimal policies; Product offerings; Product-life-cycle; Production and operations management; Quality levels; Reverse channels; Robustness and sensitivity analysis; Single product; Statistical significance; System dynamics; Two-product; Used product; Artificial intelligence; Commerce; Decision making; Decision support systems; Decision theory; Diffusers (optical); Linear matrix inequalities; Regression analysis; Sensitivity analysis; Supply chain management; Supply chains; System theory; Analysis of variance (ANOVA)",,,,,,,,,,,,"Georgiadis, P.; Industrial Management Division, P.O. Box 461, 541 24 Thessaloniki, Greece; email: geopat@auth.gr",,,03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","",Scopus,2-s2.0-70349921094
"Boran S., Goztepe K.","25958889300;25959108400;","Development of a fuzzy decision support system for commodity acquisition using fuzzy analytic network process",2010,"Expert Systems with Applications","37","3",,"1939","1945",,36,"10.1016/j.eswa.2009.07.017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-70449519154&doi=10.1016%2fj.eswa.2009.07.017&partnerID=40&md5=8ef0aace2477f376ee9159caaccdbe70","Department of Industrial Engineering, Sakarya University, Esentepe Campus, 54187 Sakarya, Turkey; Institute of Science and Technology, Sakarya University, Esentepe Campus, 54187 Sakarya, Turkey","Boran, S., Department of Industrial Engineering, Sakarya University, Esentepe Campus, 54187 Sakarya, Turkey; Goztepe, K., Institute of Science and Technology, Sakarya University, Esentepe Campus, 54187 Sakarya, Turkey","Commodity acquisition is one of the most critical tasks in a firm especially for an accountancy department. Because of imprecise and uncertain product requirements, firm accountants have to make their best effort at this stage. Determining the most critical criteria for commodity acquisition process is a vital means for a firm to balance its limited budget. Therefore, firms have used different methods to cope with this time-consuming and mentally intensive process. This study develops a fuzzy analytic network process model which may help firm accountants in this process. Results derived from a data sample are presented to exemplify the established model. Briefly, this paper proposes an intelligent approach to vendor selection through a fuzzy ANP which takes into consideration quantitative and qualitative elements in evaluating vendor alternatives. © 2009 Elsevier Ltd. All rights reserved.","Commodity acquisition; Fuzzy analytic network process (FANP); Supply chain management","Acquisition process; Best effort; Commodity acquisition; Critical tasks; Data sample; Fuzzy analytic network process; Fuzzy analytic network process (FANP); Fuzzy ANP; Fuzzy decision support system; Product requirements; Vendor Selection; Artificial intelligence; Decision support systems; Decision theory; Mergers and acquisitions; Supply chains; Supply chain management",,,,,,,,,,,,"Goztepe, K.; Institute of Science and Technology, Esentepe Campus, 54187 Sakarya, Turkey; email: kerimgoztepe@yahoo.com",,,09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-70449519154
"Collins J., Ketter W., Gini M.","55461319800;6506124372;7006205599;","Flexible decision support in dynamic inter-organisational networks",2010,"European Journal of Information Systems","19","4",,"436","448",,30,"10.1057/ejis.2010.24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955640956&doi=10.1057%2fejis.2010.24&partnerID=40&md5=03fafb49da3f1ce7eab9dc1c38618527","Department of Computer Science and Engineering, University of Minnesota, 4-192 EE/CS Building, 200 Union St. S.E., Minneapolis, MN 55455, United States; Department of Decision and Information Sciences, RSM Erasmus University, Netherlands","Collins, J., Department of Computer Science and Engineering, University of Minnesota, 4-192 EE/CS Building, 200 Union St. S.E., Minneapolis, MN 55455, United States; Ketter, W., Department of Decision and Information Sciences, RSM Erasmus University, Netherlands; Gini, M., Department of Computer Science and Engineering, University of Minnesota, 4-192 EE/CS Building, 200 Union St. S.E., Minneapolis, MN 55455, United States","An effective Decision Support System (DSS) should help its users improve decision making in complex, information-rich environments. We present a feature gap analysis that shows that current decision support technologies lack important qualities for a new generation of agile business models that require easy, temporary integration across organisational boundaries. We enumerate these qualities as DSS Desiderata, properties that can contribute both effectiveness and flexibility to users in such environments. To address this gap, we describe a new design approach that enables users to compose decision behaviours from separate, configurable components, and allows dynamic construction of analysis and modelling tools from small, single-purpose evaluator services. The result is what we call an evaluator service network that can easily be configured to test hypotheses and analyse the impact of various choices for elements of decision processes. We have implemented and tested this design in an interactive version of the MinneTAC trading agent, an agent designed for the Trading Agent Competition for Supply Chain Management. © 2010 Operational Research Society Ltd. All rights reserved.","autonomous agent; decision support; Semantic Web; service composition","Artificial intelligence; Autonomous agents; Commerce; Decision making; Quality control; Semantic Web; Supply chain management; Analysis and modelling; Decision support system (dss); Decision supports; Information-rich environments; Inter-organisational networks; Organisational boundaries; Service compositions; Trading Agent Competition; Decision support systems","National Science Foundation, NSF: NSF/IIS-0414466","Service composition supported by semantic descriptions",,,,,,,,,,"Collins, J.; Department of Computer Science and Engineering, 200 Union St. S.E., Minneapolis, MN 55455, United States; email: jcollins@cs.umn.edu",,"Palgrave Macmillan Ltd.",0960085X,,,,"English","Eur. J. Inf. Syst.",Article,"Final","",Scopus,2-s2.0-77955640956
"Quariguasi Frota Neto J., Walther G., Bloemhof J., Van Nunen J.A.E.E., Spengler T.","23093500900;22942752600;6602637314;6603697491;6701478517;","From closed-loop to sustainable supply chains: The WEEE case",2010,"International Journal of Production Research","48","15",,"4463","4481",,153,"10.1080/00207540902906151","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953628144&doi=10.1080%2f00207540902906151&partnerID=40&md5=10f3dea6d69a19416949c45487ce3790","Rotterdam School of Management (RSM), Erasmus University, 3000DR Rotterdam, Netherlands; Institute for Economics and Business Administration, Braunschweig Technical University, Braunschweig, Germany","Quariguasi Frota Neto, J., Rotterdam School of Management (RSM), Erasmus University, 3000DR Rotterdam, Netherlands; Walther, G., Institute for Economics and Business Administration, Braunschweig Technical University, Braunschweig, Germany; Bloemhof, J., Rotterdam School of Management (RSM), Erasmus University, 3000DR Rotterdam, Netherlands; Van Nunen, J.A.E.E., Rotterdam School of Management (RSM), Erasmus University, 3000DR Rotterdam, Netherlands; Spengler, T., Institute for Economics and Business Administration, Braunschweig Technical University, Braunschweig, Germany","The primary objective of closed-loop supply chains (CLSC) is to improve the maximum economic benefit from end-of-use products. Nevertheless, the literature within this stream of research advocates that closing the loop also helps to mitigate the undesirable environmental footprint of supply chains. Therefore, closed-loop supply chains are assumed to be sustainable supply chains almost by definition. In this paper we analyse if and when this assumption holds. We illustrate our findings based on the Electric and Electronic Equipment (EEE) supply chain. For all phases of the supply chain, i.e. manufacturing, usage, transportation and end-of-life activities, we assess the magnitude of the environmental impacts, based on a single environmental metric, namely the Cumulative Energy Demand (CED). Given the environmental hot-spots in the Electric and Electronic Equipment supply chain, we propose useful extensions for existing CLSC optimisation models to ensure that closed-loop supply chains are at the same time sustainable supply chains. © 2010 Taylor & Francis.","Data envelopment analysis; Decision support systems; Design for disassembly; Design for the environment; Life cycle design; Multi-criteria decision making","Closed-loop; Closed-loop supply chain; Cumulative energy demands; Decision supports; Design for the environment; Economic benefits; End-of-life; Hotspots; Life-cycle design; Multi-criteria decision making; Optimisations; Primary objective; Sustainable supply chains; Amplifiers (electronic); Artificial intelligence; Data envelopment analysis; Data handling; Decision making; Decision support systems; Decision theory; Design; Energy management; Environmental impact; Life cycle; Oscillators (electronic); Supply chain management; Supply chains",,,,,,,,,,,,"Quariguasi Frota Neto, J.; Rotterdam School of Management (RSM), , 3000DR Rotterdam, Netherlands; email: jquariguasi@rsm.nl",,,00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-77953628144
"Kumar S.K., Tiwari M.K., Babiceanu R.F.","57193118329;35427952100;8411420900;","Minimisation of supply chain cost with embedded risk using computational intelligence approaches",2010,"International Journal of Production Research","48","13",,"3717","3739",,105,"10.1080/00207540902893425","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952626327&doi=10.1080%2f00207540902893425&partnerID=40&md5=40d5e6d19b5784baf98e780d21064589","Mitsui O.S.K. Lines Pvt. Ltd., India; Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur 721302, India; Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, AR, United States","Kumar, S.K., Mitsui O.S.K. Lines Pvt. Ltd., India; Tiwari, M.K., Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur 721302, India; Babiceanu, R.F., Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, AR, United States","Global supply chains are vulnerable towards different types of risks and are dynamically expanding with the increase in globalisation. Costs are associated with every risk factor that causes disturbances in the allocation of certain goods at the required place and time, and with the required quality and quantity. In this paper, we consider a multi-echelon global supply chain model, where raw material suppliers, manufacturers, warehouses and markets are located in different countries. The paper first identifies all types of operational risk factors, their expected value and probability of occurrence, and associated additional cost. Based on initial information for the risk factors, optimal decisions regarding the inter-echelon quantity flow in the supply chain are made for a single planning horizon. Then, with the change in the expected value of the risk factors, the intra-echelon shift of flow is determined in order to minimise the total cost and risk factors. Considering the complexity involved with the problem, various computational intelligence techniques such as genetic algorithms, particle swarm optimisation and artificial bee colony are applied in the solution evaluation phase. The results obtained using the developed model illustrate that the ability to react to changes in risk factors offers potential solutions to robust supply chain design. © 2010 Taylor & Francis.","Computational intelligence techniques; Flexibility; Risk management; Supply chain management","Additional costs; Computational intelligence; Computational intelligence techniques; Developed model; Evaluation phase; Expected values; Flexibility; Global supply chain; Globalisation; Initial information; Multiechelon; Operational risks; Optimal decisions; Particle swarm optimisation; Planning horizons; Potential solutions; Probability of occurrence; Raw material suppliers; Risk factors; Supply chain costs; Supply chain design; Total costs; Artificial intelligence; Costs; Optimization; Risk management; Risks; Supply chains; Supply chain management",,,,,,,,,,,,"Babiceanu, R. F.; Department of Systems Engineering, , Little Rock, AR, United States; email: rfbabiceanu@ualr.edu",,,00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-77952626327
"Thürer M., Silva C., Stevenson M.","35811259700;55995851100;35077237500;","Workload control release mechanisms: From practice back to theory building",2010,"International Journal of Production Research","48","12",,"3593","3617",,37,"10.1080/00207540902922810","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77951100029&doi=10.1080%2f00207540902922810&partnerID=40&md5=57fbb7a66787c8340f653e7c18a76260","Institut für Werkzeugmaschinen und Fabrikbetrieb, Technical University Berlin, Pascalstr. 8-9, 10587 Berlin, Germany; Mechanical Engineering Department, University of Coimbra, Polo II Pinhal de Marrocos, 3030 Coimbra, Portugal; Department of Management Science, Lancaster University, Lancaster University Management School, Lancaster, LA1 4YX, United Kingdom","Thürer, M., Institut für Werkzeugmaschinen und Fabrikbetrieb, Technical University Berlin, Pascalstr. 8-9, 10587 Berlin, Germany; Silva, C., Mechanical Engineering Department, University of Coimbra, Polo II Pinhal de Marrocos, 3030 Coimbra, Portugal; Stevenson, M., Department of Management Science, Lancaster University, Lancaster University Management School, Lancaster, LA1 4YX, United Kingdom","Much Workload Control research has focussed on the order release stage but failed to address practical considerations that impact practical application. Order release mechanisms have been developed through simulations that neglect job size variation effects while empirical evidence suggests groups of small/large jobs are often found in practice. When job sizes vary, it is difficult to release all jobs effectivelysmall jobs favour a short period between releases and a tight workload bounding while large jobs require a longer period between releases and a slacker workload bounding. This paper represents a return from a case study setting to theory building. Through simulation, the impact of job sizes on overall performance is explored using all three aggregate load approaches. Options tested include: using distinct load capacities for small/large jobs and prioritising based on job size or routing length. Results suggest the best solution is assigning priority based on routing length; this improved performance, especially for large jobs, and allowed a short release period to be applied, as favoured by small jobs. These ideas have also been applied to a second practical problem: how to handle rush orders. Again, prioritisation, given to rush orders, leads to the best overall shop performance. © 2010 Taylor & Francis.","Decision support systems; Production control; Production planning; Shop floor control; Supply chain management","Aggregate load; Empirical evidence; Job size; Load capacity; Practical problems; Priority-based; Production Planning; Release mechanism; Routing length; Rush order; Shop floor Control; Shop performance; Short periods; Theory building; Workload control; Artificial intelligence; Decision making; Decision support systems; Decision theory; Floors; Planning; Process control; Production control; Production engineering; Project management; Supply chains; Supply chain management",,,,,,,,,,,,"Stevenson, M.; Department of Management Science, , Lancaster, LA1 4YX, United Kingdom; email: m.stevenson@lancaster.ac.uk",,,00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-77951100029
"Lauras M., Marques G., Gourc D.","19638996200;35091286500;55888571400;","Towards a multi-dimensional project Performance Measurement System",2010,"Decision Support Systems","48","2",,"342","353",,66,"10.1016/j.dss.2009.09.002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-71649097609&doi=10.1016%2fj.dss.2009.09.002&partnerID=40&md5=f3edcb0b859343852c7de0b8fa260abf","Université Toulouse / Mines Albi, Centre Génie Industriel, Campus Jarlard, Route de Teillet, 81013 Albi, France; Toulouse Business School/Department of Industrial Organisation, Logistics and Technology, 20 Boulevard Lascrosses, 31068 Toulouse Cedex 7, France","Lauras, M., Université Toulouse / Mines Albi, Centre Génie Industriel, Campus Jarlard, Route de Teillet, 81013 Albi, France, Toulouse Business School/Department of Industrial Organisation, Logistics and Technology, 20 Boulevard Lascrosses, 31068 Toulouse Cedex 7, France; Marques, G., Université Toulouse / Mines Albi, Centre Génie Industriel, Campus Jarlard, Route de Teillet, 81013 Albi, France; Gourc, D., Université Toulouse / Mines Albi, Centre Génie Industriel, Campus Jarlard, Route de Teillet, 81013 Albi, France","This paper discusses the difficulty of controlling a complex project caused by the great number of performance indicators. The problem studied is how to allow project managers to better control the performance of their projects. From a literature review we noted several critical aspects to this problem: there are many dimensions for evaluating project performance (cost, time, quality, risk, etc.); performance factors should be able to be relevantly aggregated for controlling the project, but no formalized tool exists to do this. We suggest a method to facilitate project performance analysis via a multi-criteria approach. The method focuses on three particular axes for the analysis of project performance: project task, performance indicator categories, and a breakdown of the performance triptych (Effectiveness, Efficiency, Relevance). Finally, the MACBETH method is used to aggregate performance expressions. An application case study examining a real project management situation is included to illustrate the implementation. © 2009 Elsevier B.V. All rights reserved.","Decision Support Systems; Multiple criteria analysis; Performance Measurement System; Project management; Project performance","Aggregate performance; Complex projects; Literature reviews; MACBETH; Multi-criteria approach; Multiple criteria analysis; Performance factors; Performance indicators; Performance measurement system; Project managers; Project performance; Real projects; Aggregates; Artificial intelligence; Benchmarking; Decision support systems; Decision theory; Project management; Supply chain management; Waste disposal; Decision making",,,,,,,,,,,,"Marques, G.; Université Toulouse / Mines Albi, Campus Jarlard, Route de Teillet, 81013 Albi, France; email: guillaume.marques@enstimac.fr",,,01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","All Open Access, Green",Scopus,2-s2.0-71649097609
"Arora V., Chan F.T.S., Tiwari M.K.","36883413400;7202586517;35427952100;","An integrated approach for logistic and vendor managed inventory in supply chain",2010,"Expert Systems with Applications","37","1",,"39","44",,43,"10.1016/j.eswa.2009.05.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349443216&doi=10.1016%2fj.eswa.2009.05.016&partnerID=40&md5=6ac9501dc97d7fa7143bc9746658f3da","Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur, 721302, India; Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong","Arora, V., Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur, 721302, India; Chan, F.T.S., Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong; Tiwari, M.K., Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur, 721302, India","In this paper, a quaternary policy system towards integrated logistics and inventory aspect of the supply chain has been proposed. A system of multi retailers and distributors, with each distributor following a unique policy, will be analysed. The first policy is continuous time replenishment policy where the retailers' inventory is replenished in every time interval. In the next three policies, inventory of the retailers will be replenished by some definite policy factors. The vendor managed inventory (VMI) system is used for updating the inventory of the retailers. An order-up-to policy (q, Q) is used for updating the inventory of distributors. Total erstwhile demands to the retailer will be used to determine the amount of inventory acclivity. Furthermore, the distributors will be sending the delivery vehicles to few fellow retailers who are shortlisted according to the policy, followed by the retailers and associated distributors. On the basis of random demand that the retailers are facing from end customers and the total demand that has incurred in the supply chain, products are unloaded to the selected retailers from the delivery vehicle. The path of the delivery vehicle is retrieved by dynamic ant colony optimization. In addition, a framework has been developed to measure the end-customer satisfaction level and total supply chain cost incorporating the inventory holding cost, ordering cost and the transportation cost. The framework has been numerically moulded with different settings to compare the performance of the quadruplet policies. © 2009 Elsevier Ltd. All rights reserved.","Dynamic ant colony optimization; Supply chain; Vendor managed inventory","Ant colony optimization; Artificial intelligence; Continuous time systems; Costs; Customer satisfaction; Supply chains; Vehicles; Integrated approach; Integrated logistics; Order-up-to policy; Replenishment policy; Supply chain costs; Transportation cost; Vendor managed Inventory; Vendor managed inventory systems; Sales",,,,,,,,,,,,"Chan, F.T.S.; Department of Industrial and System Engineering, , Hung Hom, Hong Kong; email: ftschan@hkucc.hku.hk",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-70349443216
"Hu G., Bidanda B.","57115681500;7003446855;","Modeling sustainable product lifecycle decision support systems",2009,"International Journal of Production Economics","122","1",,"366","375",,68,"10.1016/j.ijpe.2009.06.011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349414329&doi=10.1016%2fj.ijpe.2009.06.011&partnerID=40&md5=f53cbb7be2caa9691c285d66fb674c67","Industrial and Manufacturing Systems Engineering, Iowa State University, 3033 Black Engineering Building, Ames, IA 50011, United States; Department of Industrial Engineering, University of Pittsburgh, 1048 Benedum Hall, Pittsburgh, PA 15261, United States","Hu, G., Industrial and Manufacturing Systems Engineering, Iowa State University, 3033 Black Engineering Building, Ames, IA 50011, United States; Bidanda, B., Department of Industrial Engineering, University of Pittsburgh, 1048 Benedum Hall, Pittsburgh, PA 15261, United States","Sustainable product lifecycle systems are attracting increasing attention because of cost competition, resource constraints and environmental issues. Short lifecycle products, such as consumer and defense electronics, are of particular concern. We formulate a product lifecycle evolution system based on stochastic dynamic programming. By applying the concept of a sustainable product lifecycle system on a product line, conclusions and guidelines for rational decision making can be developed through each phase of the product life cycle.","Closed-loop supply chain; Environmental regulation; Stochastic dynamic programming; Sustainable product lifecycle","Closed-loop supply chain; Defense electronics; Environmental issues; Evolution systems; Product life cycles; Product-life-cycle; Product-lines; Rational decision making; Resource Constraint; Short lifecycle products; Stochastic dynamic programming; Sustainable product lifecycle; Sustainable products; Artificial intelligence; Consumer electronics; Decision making; Decision support systems; Decision theory; Dynamic programming; Environmental regulations; Supply chain management; Supply chains; Systems engineering; Wireless telecommunication systems; Sustainable development","Air Force Research Laboratory, AFRL","This paper is supported in part by a grant from the Air Force Research Laboratories and the Doyle Center for Manufacturing Technology.",,,,,,,,,,"Bidanda, B.; Department of Industrial Engineering, 1048 Benedum Hall, Pittsburgh, PA 15261, United States; email: bidanda@pitt.edu",,,09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-70349414329
"Chatfield D.C., Harrison T.P., Hayya J.C.","7006891030;7201365825;6701558311;","SCML: An information framework to support supply chain modeling",2009,"European Journal of Operational Research","196","2",,"651","660",,24,"10.1016/j.ejor.2008.03.027","https://www.scopus.com/inward/record.uri?eid=2-s2.0-58149469063&doi=10.1016%2fj.ejor.2008.03.027&partnerID=40&md5=c3512401541462f93e50ee8fec618f53","Department of Information Technology and Decision Sciences, College of Business and Public Administration, Virginia Modeling, Analysis, and Simulation Center (VMASC), Old Dominion University, 2063 Constant Hall, Norfolk, VA 23529, United States; Department of Supply Chain and Information Systems, Smeal College of Business, Penn State University, 459 Business Building, University Park, PA 16802, United States; Department of Supply Chain and Information Systems, Smeal College of Business, Penn State University, 441 Business Building, University Park, PA 16802, United States","Chatfield, D.C., Department of Information Technology and Decision Sciences, College of Business and Public Administration, Virginia Modeling, Analysis, and Simulation Center (VMASC), Old Dominion University, 2063 Constant Hall, Norfolk, VA 23529, United States; Harrison, T.P., Department of Supply Chain and Information Systems, Smeal College of Business, Penn State University, 459 Business Building, University Park, PA 16802, United States; Hayya, J.C., Department of Supply Chain and Information Systems, Smeal College of Business, Penn State University, 441 Business Building, University Park, PA 16802, United States","We develop an open information standard to assist supply chain modeling, analysis, and decision support. The Supply Chain Modeling Language (SCML) is a platform- and methodology-independent Extensible Markup Language (XML)-based markup language for storing supply chain structural and managerial information. SCML enables supply chain problem instance reuse and sharing, provides a common format for analytical software interoperability, and can improve the quality of the supply chain description. We develop several pieces of software to aid both users and developers in the utilization of SCML. We demonstrate SCML's applicability by developing a supply chain simulation tool (SISCO) that utilizes the SCML format. © 2008 Elsevier B.V. All rights reserved.","Decision support systems; Modeling systems and languages; Supply chain management; Supply chain modeling; XML","Administrative data processing; Artificial intelligence; Computer software; Computer software reusability; Decision making; Decision support systems; Decision theory; Hypertext systems; Linguistics; Management information systems; Markup languages; Query languages; Supply chains; Systems analysis; XML; Analytical softwares; Extensible Markup languages; Information frameworks; Information standards; Modeling systems and languages; Problem instances; Supply chain modeling; Supply chain simulations; Supply chain management","Pennsylvania State University, PSU; Training Centre for Food and Beverage Supply Chain Optimisation, Australian Research Council","We wish to thank the Center for Supply Chain Research (CSCR), Smeal College of Business, Pennsylvania State University, for its support of this work. We also wish to thank Birgir Hafsteinsson for porting the SCML parsing code from Java to .NET.",,,,,,,,,,"Chatfield, D.C.; Department of Information Technology and Decision Sciences, 2063 Constant Hall, Norfolk, VA 23529, United States; email: dchatfie@odu.edu",,,03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","",Scopus,2-s2.0-58149469063
"Repoussis P.P., Paraskevopoulos D.C., Zobolas G., Tarantilis C.D., Ioannou G.","11541227800;15020831000;23098756200;6602903945;57193985475;","A web-based decision support system for waste lube oils collection and recycling",2009,"European Journal of Operational Research","195","3",,"676","700",,52,"10.1016/j.ejor.2007.11.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-56349127944&doi=10.1016%2fj.ejor.2007.11.004&partnerID=40&md5=8cd0177eb88231eb0ec83595786085bf","Management Science Laboratory, Department of Management Science and Technology, Athens University of Economics and Business, Evelpidon 47A and Leukados 33, 11362 Athens, Greece","Repoussis, P.P., Management Science Laboratory, Department of Management Science and Technology, Athens University of Economics and Business, Evelpidon 47A and Leukados 33, 11362 Athens, Greece; Paraskevopoulos, D.C., Management Science Laboratory, Department of Management Science and Technology, Athens University of Economics and Business, Evelpidon 47A and Leukados 33, 11362 Athens, Greece; Zobolas, G., Management Science Laboratory, Department of Management Science and Technology, Athens University of Economics and Business, Evelpidon 47A and Leukados 33, 11362 Athens, Greece; Tarantilis, C.D., Management Science Laboratory, Department of Management Science and Technology, Athens University of Economics and Business, Evelpidon 47A and Leukados 33, 11362 Athens, Greece; Ioannou, G., Management Science Laboratory, Department of Management Science and Technology, Athens University of Economics and Business, Evelpidon 47A and Leukados 33, 11362 Athens, Greece","This paper presents a web-based decision support system (DSS) that enables schedulers to tackle reverse supply chain management problems interactively. The focus is on the efficient and effective management of waste lube oils collection and recycling operations. The emphasis is given on the systemic dimensions and modular architecture of the proposed DSS. The latter incorporates intra- and inter-city vehicle routing with real-life operational constraints using shortest path and sophisticated hybrid metaheuristic algorithms. It is also integrated with an Enterprise Resource Planning system allowing the utilization of particular functional modules and the combination with other peripheral planning tools. Furthermore, the proposed DSS provides a framework for on-line monitoring and reporting to all stages of the waste collection processes. The system is developed using a web architecture that enables sharing of information and algorithms among multiple sites, along with wireless telecommunication facilities. The application to an industrial environment showed improved productivity and competitiveness, indicating its applicability on realistic reverse logistical planning problems. © 2007 Elsevier B.V. All rights reserved.","Decision support systems; Vehicle routing; Waste management","Administrative data processing; Artificial intelligence; Competition; Decision making; Decision support systems; Decision theory; Enterprise resource planning; Management information systems; Recycling; Resource allocation; Routing algorithms; Solid wastes; Supply chain management; Supply chains; Vehicle routing; Decision supports; Effective managements; Enterprise Resource Planning systems; Functional modules; Industrial environments; Line monitoring; Logistical planning; Lube oils; Metaheuristic algorithms; Modular architectures; Operational constraints; Planning tools; Recycling operations; Reverse supply chain managements; Shortest paths; Waste collections; Web architectures; Waste management","GSRT NM-67","This work is supported by the General Secretariat for Research and Technology of the Hellenic Ministry of Development under contract GSRT NM-67.",,,,,,,,,,"Repoussis, P.P.; Management Science Laboratory, Evelpidon 47A and Leukados 33, 11362 Athens, Greece; email: prepousi@aueb.gr",,,03772217,,EJORD,,"English","Eur J Oper Res",Article,"Final","",Scopus,2-s2.0-56349127944
"O'Donnell T., Humphreys P., McIvor R., Maguire L.","14009609300;7005959792;7004499918;7006038431;","Reducing the negative effects of sales promotions in supply chains using genetic algorithms",2009,"Expert Systems with Applications","36","4",,"7827","7837",,22,"10.1016/j.eswa.2008.11.034","https://www.scopus.com/inward/record.uri?eid=2-s2.0-60249103682&doi=10.1016%2fj.eswa.2008.11.034&partnerID=40&md5=173a7c9eb4fee668cbea0782aee57aaa","Faculty of Business and Management, University of Ulster, Jordanstown Campus, Shore Road, Newtownabbey, Antrim BT37 0QBN, United Kingdom; ISEL, Faculty of Engineering, University of Ulster, United Kingdom","O'Donnell, T., Faculty of Business and Management, University of Ulster, Jordanstown Campus, Shore Road, Newtownabbey, Antrim BT37 0QBN, United Kingdom; Humphreys, P., Faculty of Business and Management, University of Ulster, Jordanstown Campus, Shore Road, Newtownabbey, Antrim BT37 0QBN, United Kingdom; McIvor, R., Faculty of Business and Management, University of Ulster, Jordanstown Campus, Shore Road, Newtownabbey, Antrim BT37 0QBN, United Kingdom; Maguire, L., ISEL, Faculty of Engineering, University of Ulster, United Kingdom","This paper considers the detrimental effect of promotions on the supply chain (SC), one of the main causes of the bullwhip effect. A genetic algorithm (GA) is proposed to reduce these negative effects. In order to validate the GA, it is used to determine the optimal ordering policy in an online version of the MIT beer distribution game. Subsequently, the GA is applied in a number of experiments involving deterministic and random demand and lead times combined with sales promotions. It is shown how GAs can be used to dampen the impact of the bullwhip effect and can be used to assist supply managers in predicting reorder quantities along the supply chain. © 2008 Elsevier Ltd. All rights reserved.","Artificial intelligence; Efficiency; Forecasting; Supply chain","Artificial intelligence; Efficiency; Forecasting; Sales; Supply chains; Beer distribution games; Bullwhip effects; Lead time; Online versions; Optimal ordering policy; Random demand; Sales promotions; Genetic algorithms",,,,,,,,,,,,"Humphreys, P.; Faculty of Business and Management, Jordanstown Campus, Shore Road, Newtownabbey, Antrim BT37 0QBN, United Kingdom; email: pk.humphreys@ulster.ac.uk",,"Elsevier Ltd",09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-60249103682
"Shang J., Tadikamalla P.R., Kirsch L.J., Brown L.","7101847312;6603890380;7005939498;55469600800;","A decision support system for managing inventory at GlaxoSmithKline",2008,"Decision Support Systems","46","1",,"1","13",,43,"10.1016/j.dss.2008.04.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-56049101169&doi=10.1016%2fj.dss.2008.04.004&partnerID=40&md5=ff18074ab29fde8c479172ca34e68a33","The Joseph M. Katz Graduate School of Business, University of Pittsburgh, 230 Mervis Hall, Pittsburgh, PA 15260, United States; The Joseph M. Katz Graduate School of Business, University of Pittsburgh, 258 Mervis Hall, Pittsburgh, PA 15260, United States; The Joseph M. Katz Graduate School of Business, University of Pittsburgh, 361 Mervis Hall, Pittsburgh, PA 15260, United States; GlaxoSmithKline, 1000 GSK Drive, Pittsburgh, PA 15108, United States","Shang, J., The Joseph M. Katz Graduate School of Business, University of Pittsburgh, 230 Mervis Hall, Pittsburgh, PA 15260, United States; Tadikamalla, P.R., The Joseph M. Katz Graduate School of Business, University of Pittsburgh, 258 Mervis Hall, Pittsburgh, PA 15260, United States; Kirsch, L.J., The Joseph M. Katz Graduate School of Business, University of Pittsburgh, 361 Mervis Hall, Pittsburgh, PA 15260, United States; Brown, L., GlaxoSmithKline, 1000 GSK Drive, Pittsburgh, PA 15108, United States","Firms often turn to supply chain software to streamline and standardize operations. A challenge is how to best utilize the data provided by the software. One approach is to import the data into Decision Support Systems (DSS) to build special-purposed decision aids. This paper presents an effective inventory management model for GlaxoSmithKline (GSK). The DSS effectively determines the safety stock level and the number of weeks forward coverage (WFC) for each SKU (Stock Keeping Unit). We discuss GSK's experiences relative to the literature on DSS design, implementation, and usage. This research shows implementing the proposed decision support system would provide GSK a distinct competitive advantage. However, careful implementation is necessary to fully realize the potential of the DSS. © 2008 Elsevier B.V. All rights reserved.","Consumer healthcare product; Decision Support Systems; Inventory; Spreadsheet modeling","Administrative data processing; Artificial intelligence; Competition; Decision making; Decision theory; Management information systems; Supply chains; Competitive advantages; Consumer healthcare product; Decision aids; Decision supports; Glaxosmithkline; Inventory; Inventory managements; Safety stocks; Decision support systems",,,,,,,,,,,,"Shang, J.; The Joseph M. Katz Graduate School of Business, 230 Mervis Hall, Pittsburgh, PA 15260, United States; email: Shang@katz.pitt.edu",,,01679236,,DSSYD,,"English","Decis Support Syst",Article,"Final","",Scopus,2-s2.0-56049101169
"Jain V., Benyoucef L., Deshmukh S.G.","35749011500;6506304639;7102221659;","What's the buzz about moving from 'lean' to 'agile' integrated supply chains? A fuzzy intelligent agent-based approach",2008,"International Journal of Production Research","46","23",,"6649","6677",,75,"10.1080/00207540802230462","https://www.scopus.com/inward/record.uri?eid=2-s2.0-54349127836&doi=10.1080%2f00207540802230462&partnerID=40&md5=48c20466c550758481c038139b06e5e6","INRIA-Lorraine, COSTEAM-Project, ISGMP Bat. A, Metz, France; Mechanical Engineering Department, Indian Institute of Technology Delhi, New Delhi, India","Jain, V., INRIA-Lorraine, COSTEAM-Project, ISGMP Bat. A, Metz, France; Benyoucef, L., INRIA-Lorraine, COSTEAM-Project, ISGMP Bat. A, Metz, France; Deshmukh, S.G., Mechanical Engineering Department, Indian Institute of Technology Delhi, New Delhi, India","The ability to build lean and agile supply chains has not developed as rapidly as anticipated, because the development of technology to manage such concepts of lean/agile for integrated supply chains is still under way. Also, due to ill-defined and vague indicators, which exist within leanness/agility assessment, many measures are described subjectively by linguistic terms, which are characterized by vagueness and multi-possibility, and the conventional assessment approaches cannot suitably or effectively handle such dynamic situations. In this paper, we propose a novel approach to model agility (which includes leanness) and introduce dynamic agility level index (DALi) through fuzzy intelligent agents. Generally, it is difficult to emulate human decision making if the recommendations of the agents are provided as crisp, numerical values. The multiple intelligent agents used in this study communicate their recommendation as fuzzy numbers to accommodate ambiguity in the opinion and the data used for modelling agility attributes for integrated supply chains. Moreover, when agents operate based on different criteria pertaining to agility like flexibility, profitability, quality, innovativeness, pro-activity, speed of response, cost, robustness, etc., for integrated supply chains, the ranking and aggregation of these fuzzy opinions to arrive at a consensus is complex. The proposed fuzzy intelligent agents approach provides a unique and unprecedented attempt to determine consensus in these fuzzy opinions and effectively model dynamic agility. The efficacy of the proposed approach is demonstrated with the help of an illustrative example.","Agility; Fuzzy logic; Integrated supply chains; Leanness; Multi-agents","Agents; Agglomeration; Artificial intelligence; Computer software; Decision making; Decision support systems; Fuzzy logic; Fuzzy sets; Intelligent agents; Linguistics; Mathematical models; Problem solving; Supply chain management; Work simplification; Agile supply chains; Agility; Assessment approaches; Dynamic situations; Fuzzy numbers; Human decision makings; Illustrative examples; Innovativeness; Integrated supply chains; Leanness; Linguistic terms; Model dynamics; Modelling; Multi-agents; Numerical values; Speed of responses; Supply chains",,,,,,,,,,,,"Benyoucef, L.; INRIA-Lorraine, , Metz, France; email: lyes.benyoucef@loria.fr",,,00207543,,IJPRB,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-54349127836
"Baptiste P., Alsene É., Gaudimier R.","57207520892;56618411800;25824851500;","Integration of production and shipping planning: A co-operative approach",2008,"Production Planning and Control","19","7",,"645","654",,6,"10.1080/09537280802571613","https://www.scopus.com/inward/record.uri?eid=2-s2.0-57349175611&doi=10.1080%2f09537280802571613&partnerID=40&md5=00d58cf518866de6710179a25eca78d9","Department of Mathematics and Industrial Engineering, École Polytechnique of Montreal, QC, Canada","Baptiste, P., Department of Mathematics and Industrial Engineering, École Polytechnique of Montreal, QC, Canada; Alsene, É., Department of Mathematics and Industrial Engineering, École Polytechnique of Montreal, QC, Canada; Gaudimier, R., Department of Mathematics and Industrial Engineering, École Polytechnique of Montreal, QC, Canada","This article deals with the integration of the production planning and the shipping planning processes in a real company within a just-in-time environment. Perfect integration between production and shipping is needed because 2500 m3 of products are daily sent to many different destinations using few outgoing doors. Most existing papers focus on the scheduling aspects alone, and assume that a product is shipped to a single destination. We propose a decision support system (DSS) to schedule simultaneously the production and the semi-trailers used for shipping. This DSS shares the decision between the user and a computer, and focuses on the organisational aspect of scheduling. This DSS creates incrementally a solution by considering successively the critical aspects and the criteria of each actor. This original integration approach is tested on the industrial case and compared to more classical approaches.","Decision support system; Integration; Production planning; Shipping planning","Administrative data processing; Artificial intelligence; Decision making; Decision support systems; Decision theory; Management information systems; Planning; Production control; Production engineering; Project management; Scheduling; Supply chains; Classical approaches; Decision support system; Decision supports; Integration approaches; Just in times; Organisational aspects; Planning processes; Production planning; Semi-trailers; Shipping planning; To many; Just in time production",,,,,,,,,,,,"Baptiste, P.; Department of Mathematics and Industrial Engineering, , QC, Canada; email: pbaptiste@polymtl.ca",,,09537287,,PPCOE,,"English","Prod Plann Control",Article,"Final","",Scopus,2-s2.0-57349175611
"Thammakoranonta N., Radhakrishnan A., Davis S., Peck J.C., Miller J.L.","24559131100;23975067100;7405958602;7102796433;55568515132;","A protocol for the order commitment decision in a supply network",2008,"International Journal of Production Economics","115","2",,"515","527",,6,"10.1016/j.ijpe.2008.05.017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-55349091316&doi=10.1016%2fj.ijpe.2008.05.017&partnerID=40&md5=b98bd030ce4cac631e5456e37112df51","Department of Management, Clemson University, 101 Sirrine Hall, Clemson, SC 29634-1305, United States; Department of Information Science and Systems, Morgan State University, MD, United States; Department of Computer Science, Clemson University, SC, United States","Thammakoranonta, N., Department of Management, Clemson University, 101 Sirrine Hall, Clemson, SC 29634-1305, United States; Radhakrishnan, A., Department of Information Science and Systems, Morgan State University, MD, United States; Davis, S., Department of Management, Clemson University, 101 Sirrine Hall, Clemson, SC 29634-1305, United States; Peck, J.C., Department of Computer Science, Clemson University, SC, United States; Miller, J.L., Department of Management, Clemson University, 101 Sirrine Hall, Clemson, SC 29634-1305, United States","Timely and accurate order commitment decisions are important to supply networks. Many firms face problems such as order over-commitment and inability to fulfill committed orders. Addressing shortcomings in the state-of-the- practice, we developed a protocol based upon the distributed database two-phase commit protocol that guarantees the atomicity of global transactions. We developed extensions of the basic protocol based on requirements of experienced supply chain professionals. Supply chain managers experienced with order promising evaluated the protocol. They opined that adoption of this protocol would reduce the decision-making time by 80%, enhance the accuracy by 30% and also improve customer service levels. © 2008 Elsevier B.V. All rights reserved.","Decision support systems; Information systems; Inventory; Protocol; Supply chain management","Artificial intelligence; Decision support systems; Information management; Information systems; Network protocols; Query languages; Supply chain management; Basic protocols; Customer service levels; Distributed database; Inventory; Order commitment; Order promising; State of the practice; Two phase commit protocols; Decision making","National Science Foundation, NSF: DMI-0075608","This work was partially supported by The National Science Foundation (Grant DMI-0075608) and by the Institute for Supply Management.",,,,,,,,,,"Davis, S.; Department of Management, , Clemson, SC 29634-1305, United States; email: davis@clemson.edu",,"Elsevier",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-55349091316
"Schneeweiss C.","6701686796;","Distributed decision making in supply chain management",2003,"International Journal of Production Economics","84","1",,"71","83",,58,"10.1016/S0925-5273(02)00381-X","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0037432535&doi=10.1016%2fS0925-5273%2802%2900381-X&partnerID=40&md5=2bef67875c243d2734aed45ada5f364c","Lehrstuhl fur Unternehmensplanung, Insb. Operations Research, University of Mannheim, P.O. Box 10 32 64, D-68131 Mannheim, Germany","Schneeweiss, C., Lehrstuhl fur Unternehmensplanung, Insb. Operations Research, University of Mannheim, P.O. Box 10 32 64, D-68131 Mannheim, Germany","The paper identifies different classes of distributed decision making problems in supply chain management. Since, these problem classes have been developed in various sciences like applied mathematics, operations research, economics and artificial intelligence, the paper gives a systematic overview as to the specific contribution each single science is providing, particularly indicating possible synergies. Moreover, it points to those distributed decision-making problems that prove to be of major relevance for supply chain management. © 2002 Elsevier Science B.V. All rights reserved.","Coordination; Distributed decision making; Supply chain management","Artificial intelligence; Decision making; Industrial management; Industrial relations; Logistics; Operations research; Supply chain management (SCM); Industrial economics",,,,,,,,,,,,"Schneeweiss, C.; Lehrstuhl fur Unternehmensplanung, P.O. Box 10 32 64, D-68131 Mannheim, Germany; email: schneeweiss@bwl.uni-mannheim.de",,"Elsevier",09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-0037432535
"Giannoccaro I., Pontrandolfo P.","6506185365;6602637692;","Inventory management in supply chains: A reinforcement learning approach",2002,"International Journal of Production Economics","78","2",,"153","161",,111,"10.1016/S0925-5273(00)00156-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0037151232&doi=10.1016%2fS0925-5273%2800%2900156-0&partnerID=40&md5=f5583b249764c8ba0761bbd110612df3","Dipartimento di Ingegneria Meccanica e Gestionale, Politecnico di Bari, Viale Japigia 182, 70123 Bari, Italy","Giannoccaro, I., Dipartimento di Ingegneria Meccanica e Gestionale, Politecnico di Bari, Viale Japigia 182, 70123 Bari, Italy; Pontrandolfo, P., Dipartimento di Ingegneria Meccanica e Gestionale, Politecnico di Bari, Viale Japigia 182, 70123 Bari, Italy","A major issue in supply chain inventory management is the coordination of inventory policies adopted by different supply chain actors, such as suppliers, manufacturers, distributors, so as to smooth material flow and minimize costs while responsively meeting customer demand. This paper presents an approach to manage inventory decisions at all stages of the supply chain in an integrated manner. It allows an inventory order policy to be determined, which is aimed at optimizing the performance of the whole supply chain. The approach consists of three techniques: (i) Markov decision processes (MDP) and (ii) an artificial intelligent algorithm to solve MDPs, which is based on (iii) simulation modeling. In particular, the inventory problem is modeled as an MDP and a reinforcement learning (RL) algorithm is used to determine a near optimal inventory policy under an average reward criterion. RL is a simulation-based stochastic technique that proves very efficient particularly when the MDP size is large. © 2002 Elsevier Science B.V. All rights reserved.","Inventory management; Markov decision processes; Reinforcement learning; Supply chain","Algorithms; Artificial intelligence; Computer simulation; Costs; Decision making; Industrial management; Inventory control; Markov processes; Mathematical models; Optimization; Problem solving; Public policy; Raw materials; Reinforcement learning; Supply chains; Production engineering",,,,,,,,,,,,"Pontrandolfo, P.; Dipartimento di IMG, Viale Japigia 182, 70123 Bari, Italy; email: pontrandolfo@poliba.it",,,09255273,,IJPCE,,"English","Int J Prod Econ",Article,"Final","",Scopus,2-s2.0-0037151232
"Pontrandolfo P., Gosavi A., Okogbaa O.G., Das T.K.","6602637692;6603595178;55952244900;57218946470;","Global supply chain management: A reinforcement learning approach",2002,"International Journal of Production Research","40","6",,"1299","1317",,61,"10.1080/00207540110118640","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0037091951&doi=10.1080%2f00207540110118640&partnerID=40&md5=383baa1ab8db99dacbb5946da728e592","Dipartimento di Progettazione e Produzione Industriale, Politecnico di Bari, Viale Japigia 182, I-70126 Bari, Italy; Industrial Engineering Program, University of Southern Colorado, 261, Technology Building, 2200 Bonforte Boulevard, Pueblo, CO 81001, United States; College of Engineering, Department of Industrial and Management Systems Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33647, United States","Pontrandolfo, P., Dipartimento di Progettazione e Produzione Industriale, Politecnico di Bari, Viale Japigia 182, I-70126 Bari, Italy; Gosavi, A., Industrial Engineering Program, University of Southern Colorado, 261, Technology Building, 2200 Bonforte Boulevard, Pueblo, CO 81001, United States; Okogbaa, O.G., College of Engineering, Department of Industrial and Management Systems Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33647, United States; Das, T.K., College of Engineering, Department of Industrial and Management Systems Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33647, United States","In recent years, researchers and practitioners alike have devoted a great deal of attention to supply chain management (SCM). The main focus of SCM is the need to integrate operations along the supply chain as part of an overall logistic support function. At the same time, the need for globalization requires that the solution of SCM problems be performed in an international context as part of what we refer to as Global Supply Chain Management (GSCM). This paper proposes an approach to study GSCM problems using an artificial intelligence framework called reinforcement learning (RL). The RL framework allows the management of global supply chains under an integration perspective. The RL approach has remarkable similarities to that of an autonomous agent network (AAN); a similarity that we shall discuss. The RL approach is applied to a case example, namely a networked production system that spans several geographic areas and logistics stages. We discuss the results and provide guidelines and implications for practical applications.",,"Artificial intelligence; Computer aided manufacturing; Distributed computer systems; Intelligent agents; International trade; Learning systems; Autonomous agent network; Global supply chain management; Reinforcement learning; Industrial management",,,,,,,,,,,,,,,00207543,,,,"English","Int J Prod Res",Article,"Final","",Scopus,2-s2.0-0037091951
"Swaminathan J.M., Smith S.F., Sadeh N.M.","7004428056;7406648499;6603834332;","Modeling supply chain dynamics: A multiagent approach",1998,"Decision Sciences","29","3",,"607","632",,628,"10.1111/j.1540-5915.1998.tb01356.x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032268192&doi=10.1111%2fj.1540-5915.1998.tb01356.x&partnerID=40&md5=a51c248e39a62dd614c986152e077d7f","Walter A. Haas School of Business, University of California, Berkeley, CA 94720, United States; Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, United States","Swaminathan, J.M., Walter A. Haas School of Business, University of California, Berkeley, CA 94720, United States; Smith, S.F., Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, United States; Sadeh, N.M., Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, United States","A global economy and increase in customer expectations in terms of cost and services have put a premium on effective supply chain reengineering. It is essential to perform risk-benefit analysis of reengineering alternatives before making a final decision. Simulation provides an effective pragmatic approach to detailed analysis and evaluation of supply chain design and management alternatives. However, the utility of this methodology is hampered by the time and effort required to develop models with sufficient fidelity to the actual supply chain of interest. In this paper, we describe a supply chain modeling framework designed to overcome this difficulty. Using our approach, supply chain models are composed from software components that represent types of supply chain agents (e.g., retailers, manufacturers, transporters), their constituent control elements (e.g., inventory policy), and their interaction protocols (e.g., message types). The underlying library of supply chain modeling components has been derived from analysis of several different supply chains. It provides a reusable base of domain-specific primitives that enables rapid development of customized decision support tools.","And supply chain management; Artificial intelligence; Decision support system; Simulation",,,,,,,,,,,,,"Swaminathan, J.M.; Walter A. Haas School of Business, , Berkeley, CA 94720, United States; email: msj@haas.berkeley.edu",,"Decision Sciences Institute",00117315,,,,"English","Decis. Sci.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-0032268192