Research Trends and Knowledge Taxonomy of Artificial Intelligence Applications in Supply Chain Management, Logistics, and Transportation: A Systematic Literature Review and Bibliometric Analysis

Mohamed Kriouich, Hicham Sarir, Soulaiman Louah

Abstract


Due to industrialization and globalization, supply chains (SC) have become more and more in need of artificial intelligence (AI), which has sparked conversations on how to use it to improve SC performance globally. Using both quantitative and qualitative methodologies, this study provides a thorough examination of the trends, gaps, and knowledge structure in the literature on AI in SC. Scientific mapping was used to summarize 140 important publications published between 1998 and 2022. Publication years, attribution, journal co-citations, partnerships between countries and institutions, significant papers, related keywords, and historical study groups were all included in the bibliographic analysis. A thematic categorization of the data produced 22 sub-branches of AI application in SC that are covered in five domains: environment, planning and risk management, SC areas, technology, logistics and transportation, and planning and environment. The study identifies current knowledge gaps and recommends future research directions due to limited international cooperation and inadequate platforms for advancing technology research. these findings aid academics and practitioners by providing a coherent intellectual outlook on AI's involvement in SC.

Keywords


Supply Chain; Supply Chain Management; Literature Review; Bibliometric Analysis; Taxonomy; Logistics; Risk Management; Transportation.

Full Text:

PDF

References


S. Fosso Wamba, M. M. Queiroz, C. Guthrie, and A. Braganza, “Industry experiences of artificial intelligence (AI): benefits and challenges in operations and supply chain management,” Production planning & control, vol. 33, no. 16, pp. 1493-1497, 2021, doi: 10.1080/09537287.2021.1882695.

F. Schiavone and S. Sprenger, “Operations management and digital technologies,” Production Planning and Control, vol. 28, no. 16, pp. 1281–1283, Dec. 10, 2017, doi: 10.1080/09537287.2017.1375151.

M. Pournader, H. Ghaderi, A. Hassanzadegan, and B. Fahimnia, “Artificial intelligence applications in supply chain management,” International Journal of Production Economics, vol. 241, 2021, doi: 10.1016/j.ijpe.2021.108250.

D. Schutzer, “Business Expert Systems: The Competitive Edge,” Expert Systems with Applications, vol. 1, no. 1, pp. 17-21, 1990.

Z. Seyedghorban, H. Tahernejad, R. Meriton, and G. Graham, “Supply chain digitalization: past, present and future,” Production Planning and Control, vol. 31, no. 2–3, pp. 96–114, Feb. 2020, doi: 10.1080/09537287.2019.1631461.

P. Helo and Y. Hao, “Artificial intelligence in operations management and supply chain management: an exploratory case study,” Production Planning and Control, vol. 33, no. 16, pp. 1573-1590, 2021, doi: 10.1080/09537287.2021.1882690.

R. Toorajipour, V. Sohrabpour, A. Nazarpour, P. Oghazi, and M. Fischl, “Artificial intelligence in supply chain management: A systematic literature review,” J Bus Res, vol. 122, pp. 502–517, Jan. 2021, doi: 10.1016/j.jbusres.2020.09.009.

Y. Riahi, T. Saikouk, A. Gunasekaran, and I. Badraoui, “Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions,” Expert Systems with Applications, vol. 173, 2021, doi: 10.1016/j.eswa.2021.114702.

H. Min, “Artificial intelligence in supply chain management: Theory and applications,” International Journal of Logistics Research and Applications, vol. 13, no. 1, pp. 13–39, 2010, doi: 10.1080/13675560902736537.

R. Ren, W. Hu, J. Dong, B. Sun, Y. Chen, and Z. Chen, “A systematic literature review of green and sustainable logistics: Bibliometric analysis, research trend and knowledge taxonomy,” International Journal of Environmental Research and Public Health, vol. 17, no. 1, 2020, doi: 10.3390/ijerph17010261.

N. J. van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, 2010, doi: 10.1007/s11192-009-0146-3.

T. T. P. Bui, N. Domingo, C. MacGregor, and S. Wilkinson, “Zero carbon refurbishment for existing buildings: A literature review,” IOP Conf Ser Earth Environ Sci, vol. 1101, no. 2, p. 022017, Nov. 2022, doi: 10.1088/1755-1315/1101/2/022017.

N. J. van Eck and L. Waltman, “Visualizing Bibliometric Networks,” in Measuring Scholarly Impact, pp. 285–320, 2014, doi: 10.1007/978-3-319-10377-8_13.

R. Jin, H. Yuan, and Q. Chen, “Science mapping approach to assisting the review of construction and demolition waste management research published between 2009 and 2018,” Resources, Conservation and Recycling, vol. 140, pp. 175–188, 2019, doi: 10.1016/j.resconrec.2018.09.029.

A. A. Chadegani et al., “A comparison between two main academic literature collections: Web of science and scopus databases,” Asian Soc Sci, vol. 9, no. 5, pp. 18–26, Apr. 2013, doi: 10.5539/ass.v9n5p18.

T. Skjølsvik, F. Pemer, and B. R. Løwendahl, “Strategic management of professional service firms: Reviewing ABS journals and identifying key research themes,” Journal of Professions and Organization, vol. 4, no. 3, pp. 203–239, Oct. 2017, doi: 10.1093/jpo/jox005.

M. Yazdani, P. Zarate, A. Coulibaly, and E. K. Zavadskas, “A group decision making support system in logistics and supply chain management,” Expert Syst Appl, vol. 88, pp. 376–392, Dec. 2017, doi: 10.1016/j.eswa.2017.07.014.

V. Arora, F. T. S. Chan, and M. K. Tiwari, “An integrated approach for logistic and vendor managed inventory in supply chain,” Expert Syst Appl, vol. 37, no. 1, pp. 39–44, 2010, doi: 10.1016/j.eswa.2009.05.016.

S. Kumar, “A knowledge based reliability engineering approach to manage product safety and recalls,” Expert Syst Appl, vol. 41, no. 11, pp. 5323–5339, Sep. 2014, doi: 10.1016/j.eswa.2014.03.007.

P. Baptiste, É. Alsene, and R. Gaudimier, “Integration of production and shipping planning: A co-operative approach,” Production Planning and Control, vol. 19, no. 7, pp. 645–654, Oct. 2008, doi: 10.1080/09537280802571613.

L. Ferreira and D. Borenstein, “A fuzzy-Bayesian model for supplier selection,” Expert Syst Appl, vol. 39, no. 9, pp. 7834–7844, Jul. 2012, doi: 10.1016/j.eswa.2012.01.068.

F. Dweiri, S. Kumar, S. A. Khan, and V. Jain, “Designing an integrated AHP based decision support system for supplier selection in automotive industry,” Expert Syst Appl, vol. 62, pp. 273–283, Nov. 2016, doi: 10.1016/j.eswa.2016.06.030.

J. M. Swaminathan, W. A. Haas, S. F. Smith, and N. M. Sadeh, “Modeling Supply Chain Dynamics: A Multiagent Approach,” Decision sciences, vol. 29, no. 3, pp. 607-632, 1998.

A. A. Tako and S. Robinson, “The application of discrete event simulation and system dynamics in the logistics and supply chain context,” Decis Support Syst, vol. 52, no. 4, pp. 802–815, Mar. 2012, doi: 10.1016/j.dss.2011.11.015.

K. Zimmer, M. Fröhling, and F. Schultmann, “Sustainable supplier management - A review of models supporting sustainable supplier selection, monitoring and development,” Int J Prod Res, vol. 54, no. 5, pp. 1412–1442, Mar. 2016, doi: 10.1080/00207543.2015.1079340.

G. Baryannis, S. Validi, S. Dani, and G. Antoniou, “Supply chain risk management and artificial intelligence: state of the art and future research directions,” International Journal of Production Research, vol. 57, no. 7, pp. 2179–2202, 2019, doi: 10.1080/00207543.2018.1530476.

M. Lezoche, H. Panetto, J. Kacprzyk, J. E. Hernandez, and M. M. E. Alemany Díaz, “Agri-food 4.0: A survey of the Supply Chains and Technologies for the Future Agriculture,” Computers in Industry, vol. 117, 2020, doi: 10.1016/j.compind.2020.103187.

Y. Hou, M. Khokhar, S. Zia, and A. Sharma, “Assessing the best supplier selection criteria in supply chain management during the COVID-19 pandemic,” Frontiers in Psychology, vol. 12, p. 804954, 2022.

J. Quariguasi Frota Neto, G. Walther, J. Bloemhof, J. A. E. E. van Nunen, and T. Spengler, “From closed-loop to sustainable supply chains: The WEEE case,” Int J Prod Res, vol. 48, no. 15, pp. 4463–4481, Jan. 2010, doi: 10.1080/00207540902906151.

Z. X. Guo, E. W. T. Ngai, C. Yang, and X. Liang, “An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment,” Int J Prod Econ, vol. 159, pp. 16–28, Jan. 2015, doi: 10.1016/j.ijpe.2014.09.004.

A. A. Syntetos, Z. Babai, J. E. Boylan, S. Kolassa, and K. Nikolopoulos, “Supply chain forecasting: Theory, practice, their gap and the future,” European Journal of Operational Research, vol. 252, no. 1, pp. 1–26, 2016, doi: 10.1016/j.ejor.2015.11.010.

B. Latha Shankar, S. Basavarajappa, J. C. H. Chen, and R. S. Kadadevaramath, “Location and allocation decisions for multi-echelon supply chain network - A multi-objective evolutionary approach,” Expert Syst Appl, vol. 40, no. 2, pp. 551–562, Feb. 2013, doi: 10.1016/j.eswa.2012.07.065.

R. Dubey et al., “Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations,” Int J Prod Econ, vol. 226, Aug. 2020, doi: 10.1016/j.ijpe.2019.107599.

I. Giannoccaro and P. Pontrandolfo, “Inventory management in supply chains: a reinforcement learning approach,” International Journal of Production Economics, vol. 78, no. 2, pp. 153-161, 2002.

S. K. Kumar, M. K. Tiwari, and R. F. Babiceanu, “Minimisation of supply chain cost with embedded risk using computational intelligence approaches,” Int J Prod Res, vol. 48, no. 13, pp. 3717–3739, Jan. 2010, doi: 10.1080/00207540902893425.

P. Georgiadis and E. Athanasiou, “Flexible long-term capacity planning in closed-loop supply chains with remanufacturing,” Eur J Oper Res, vol. 225, no. 1, pp. 44–58, Feb. 2013, doi: 10.1016/j.ejor.2012.09.021.

W. Hu, J. Dong, B. gang Hwang, R. Ren, and Z. Chen, “A scientometrics review on city logistics literature: Research trends, advanced theory and practice,” Sustainability (Switzerland), vol. 11, no. 10, 2019, doi: 10.3390/su11102724.

S. Kumar, “A knowledge based reliability engineering approach to manage product safety and recalls,” Expert Syst Appl, vol. 41, no. 11, pp. 5323–5339, Sep. 2014, doi: 10.1016/j.eswa.2014.03.007.

L. di Giacomo and G. Patrizi, “Methodological analysis of supply chains management applications,” Eur J Oper Res, vol. 207, no. 1, pp. 249–257, Nov. 2010, doi: 10.1016/j.ejor.2010.05.003.

S. Theißen and S. Spinler, “Strategic analysis of manufacturer-supplier partnerships: An ANP model for collaborative CO2 reduction management,” Eur J Oper Res, vol. 233, no. 2, pp. 383–397, Mar. 2014, doi: 10.1016/j.ejor.2013.08.023.

S. C. Lenny Koh et al., “Decarbonising product supply chains: Design and development of an integrated evidence-based decision support system-the supply chain environmental analysis tool (SCEnAT),” Int J Prod Res, vol. 51, no. 7, pp. 2092–2109, Apr. 2013, doi: 10.1080/00207543.2012.705042.

H. Ren et al., “A GIS-based green supply chain model for assessing the effects of carbon price uncertainty on plastic recycling,” Int J Prod Res, vol. 58, no. 6, pp. 1705–1723, Mar. 2020, doi: 10.1080/00207543.2019.1693656.

G. Hu and B. Bidanda, “Modeling sustainable product lifecycle decision support systems,” Int J Prod Econ, vol. 122, no. 1, pp. 366–375, Nov. 2009, doi: 10.1016/j.ijpe.2009.06.011.

F. Olan, S. Liu, J. Suklan, U. Jayawickrama, and E. Arakpogun, “The role of Artificial Intelligence networks in sustainable supply chain finance for food and drink industry,” Int J Prod Res, vol. 60, no. 14, pp. 4418-4433, 2022, doi: 10.1080/00207543.2021.1915510.

E. B. Tirkolaee, A. Goli, P. Ghasemi, and F. Goodarzian, “Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: Pareto-based algorithms,” J Clean Prod, vol. 333, Jan. 2022, doi: 10.1016/j.jclepro.2021.130056.

V. N. S. A. Kumar, V. Kumar, M. Brady, J. A. Garza-Reyes, and M. Simpson, “Resolving forward-reverse logistics multi-period model using evolutionary algorithms,” Int J Prod Econ, vol. 183, pp. 458–469, Jan. 2017, doi: 10.1016/j.ijpe.2016.04.026.

G. Lechner and M. Reimann, “Integrated decision-making in reverse logistics: an optimisation of interacting acquisition, grading and disposition processes,” Int J Prod Res, vol. 58, no. 19, pp. 5786–5805, Oct. 2020, doi: 10.1080/00207543.2019.1659518.

F. Schätter, O. Hansen, M. Wiens, and F. Schultmann, “A decision support methodology for a disaster-caused business continuity management,” Decis Support Syst, vol. 118, pp. 10–20, Mar. 2019, doi: 10.1016/j.dss.2018.12.006.

R. Mogre, S. S. Talluri, and F. Damico, “A decision framework to mitigate supply chain risks: An application in the offshore-wind industry,” IEEE Trans Eng Manag, vol. 63, no. 3, pp. 316–325, Aug. 2016, doi: 10.1109/TEM.2016.2567539.

S. Wruck, I. F. A. Vis, and J. Boter, “Risk control for staff planning in e-commerce warehouses,” Int J Prod Res, vol. 55, no. 21, pp. 6453–6469, Nov. 2017, doi: 10.1080/00207543.2016.1207816.

D. Bogataj, M. Bogataj, and D. Hudoklin, “Mitigating risks of perishable products in the cyber-physical systems based on the extended MRP model,” Int J Prod Econ, vol. 193, pp. 51–62, Nov. 2017, doi: 10.1016/j.ijpe.2017.06.028.

H. Flores and J. R. Villalobos, “A stochastic planning framework for the discovery of complementary, agricultural systems,” Eur J Oper Res, vol. 280, no. 2, pp. 707–729, Jan. 2020, doi: 10.1016/j.ejor.2019.07.053.

K. B. Kallestrup, L. H. Lynge, R. Akkerman, and T. A. Oddsdottir, “Decision support in hierarchical planning systems: The case of procurement planning in oil refining industries,” Decis Support Syst, vol. 68, pp. 49–63, 2014, doi: 10.1016/j.dss.2014.09.003.

E. Brevik, A. Lauen, M. C. B. Rolke, K. Fagerholt, and J. R. Hansen, “Optimisation of the broiler production supply chain,” Int J Prod Res, vol. 58, no. 17, pp. 5218–5237, Sep. 2020, doi: 10.1080/00207543.2020.1713415.

H. Sarir, “Planning And Inventory Control Based On Identfication System And Pid/ Lqr Controller,” J Theor Appl Inf Technol, vol. 31, p. 24, 2020.

P. Ghandforoush and T. K. Sen, “A DSS to manage platelet production supply chain for regional blood centers,” Decis Support Syst, vol. 50, no. 1, pp. 32–42, Dec. 2010, doi: 10.1016/j.dss.2010.06.005.

W. Yang and R. Y. K. Fung, “An available-to-promise decision support system for a multi-site make-to-order production system,” Int J Prod Res, vol. 52, no. 14, pp. 4253–4266, 2014, doi: 10.1080/00207543.2013.877612.

M. Çimen and C. Kirkbride, “Approximate dynamic programming algorithms for multidimensional flexible production-inventory problems,” Int J Prod Res, vol. 55, no. 7, pp. 2034–2050, Apr. 2017, doi: 10.1080/00207543.2016.1264643.

A. S. Erdem and E. Göen, “Development of a decision support system for supplier evaluation and order allocation,” Expert Syst Appl, vol. 39, no. 5, pp. 4927–4937, Apr. 2012, doi: 10.1016/j.eswa.2011.10.024.

F. Kellner, B. Lienland, and S. Utz, “An a posteriori decision support methodology for solving the multi-criteria supplier selection problem,” Eur J Oper Res, vol. 272, no. 2, pp. 505–522, Jan. 2019, doi: 10.1016/j.ejor.2018.06.044.

J. P. Chang, Z. S. Chen, X. J. Wang, L. Martínez, W. Pedrycz, and M. J. Skibniewski, “Requirement-driven sustainable supplier selection: Creating an integrated perspective with stakeholders' interests and the wisdom of expert crowds,” Computers & Industrial Engineering, vol. 175, p. 108903, 2023.

K. I. Nikolopoulos, M. Z. Babai, and K. Bozos, “Forecasting supply chain sporadic demand with nearest neighbor approaches,” Int J Prod Econ, vol. 177, pp. 139–148, Jul. 2016, doi: 10.1016/j.ijpe.2016.04.013.

J. Hong, A. Diabat, V. v. Panicker, and S. Rajagopalan, “A two-stage supply chain problem with fixed costs: An ant colony optimization approach,” Int J Prod Econ, vol. 204, pp. 214–226, Oct. 2018, doi: 10.1016/j.ijpe.2018.07.019.

C. Mascle and J. Gosse, “Inventory management maximization based on sales forecast: Case study,” Production Planning and Control, vol. 25, no. 12, pp. 1039–1057, Sep. 2014, doi: 10.1080/09537287.2013.805343.

P. Wanke, H. Alvarenga, H. Correa, A. Hadi-Vencheh, and M. A. K. Azad, “Fuzzy inference systems and inventory allocation decisions: Exploring the impact of priority rules on total costs and service levels,” Expert Syst Appl, vol. 85, pp. 182–193, Nov. 2017, doi: 10.1016/j.eswa.2017.05.043.

H. Sarir and B. Abderhmane, “Smart inventory control by using PID ACO controller and fuzzy logic controller,” in 2022 IEEE 14th International Conference of Logistics and Supply Chain Management, LOGISTIQUA 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/LOGISTIQUA55056.2022.9938044.

T. Warren Liao and P. C. Chang, “Impacts of forecast, inventory policy, and lead time on supply chain inventoryA numerical study,” Int J Prod Econ, vol. 128, no. 2, pp. 527–537, Dec. 2010, doi: 10.1016/j.ijpe.2010.07.002.

T. O’Donnell, P. Humphreys, R. McIvor, and L. Maguire, “Reducing the negative effects of sales promotions in supply chains using genetic algorithms,” Expert Syst Appl, vol. 36, no. 4, pp. 7827–7837, 2009, doi: 10.1016/j.eswa.2008.11.034.

I. Eluubek kyzy, H. Song, A. Vajdi, Y. Wang, and J. Zhou, “Blockchain for consortium: A practical paradigm in agricultural supply chain system,” Expert Syst Appl, vol. 184, Dec. 2021, doi: 10.1016/j.eswa.2021.115425.

E. Badakhshan, P. Humphreys, L. Maguire, and R. McIvor, “Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain,” Int J Prod Res, vol. 58, no. 17, pp. 5253–5279, Sep. 2020, doi: 10.1080/00207543.2020.1715505.

S. J.-Sharahi and K. Khalili-Damghani, “Fuzzy type-II De-Novo programming for resource allocation and target setting in network data envelopment analysis: A natural gas supply chain,” Expert Syst Appl, vol. 117, pp. 312–329, Mar. 2019, doi: 10.1016/j.eswa.2018.09.046.

G. Dellino, T. Laudadio, R. Mari, N. Mastronardi, and C. Meloni, “A reliable decision support system for fresh food supply chain management,” Int J Prod Res, vol. 56, no. 4, pp. 1458–1485, Feb. 2018, doi: 10.1080/00207543.2017.1367106.

P. Priore, B. Ponte, R. Rosillo, and D. de la Fuente, “Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments,” Int J Prod Res, vol. 57, no. 11, pp. 3663–3677, Jun. 2019, doi: 10.1080/00207543.2018.1552369.

Y. Chang, A. L. Erera, and C. C. White, “Risk Assessment of Deliberate Contamination of Food Production Facilities,” IEEE Trans Syst Man Cybern Syst, vol. 47, no. 3, pp. 381–393, Mar. 2017, doi: 10.1109/TSMC.2015.2500822.

A. B. Borade and E. Sweeney, “Decision support system for vendor managed inventory supply chain: A case study,” Int J Prod Res, vol. 53, no. 16, pp. 4789–4818, Aug. 2015, doi: 10.1080/00207543.2014.993047.

J. A. Rodger, “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,” Expert Syst Appl, vol. 41, no. 16, pp. 7005–7022, Nov. 2014, doi: 10.1016/j.eswa.2014.05.012.

A. Roozbeh Nia, M. Hemmati Far, and S. T. Akhavan Niaki, “A fuzzy vendor managed inventory of multi-item economic order quantity model under shortage: An ant colony optimization algorithm,” Int J Prod Econ, vol. 155, pp. 259–271, 2014, doi: 10.1016/j.ijpe.2013.07.017.

B. Latha Shankar, S. Basavarajappa, R. S. Kadadevaramath, and J. C. H. Chen, “A bi-objective optimization of supply chain design and distribution operations using non-dominated sorting algorithm: A case study,” Expert Syst Appl, vol. 40, no. 14, pp. 5730–5739, 2013, doi: 10.1016/j.eswa.2013.03.047.

I. Moon, J. H. Lee, and J. Seong, “Vehicle routing problem with time windows considering overtime and outsourcing vehicles,” Expert Syst Appl, vol. 39, no. 18, pp. 13202–13213, Dec. 2012, doi: 10.1016/j.eswa.2012.05.081.

T. Qu, G. Q. Huang, Y. Zhang, and Q. Y. Dai, “A generic analytical target cascading optimization system for decentralized supply chain configuration over supply chain grid,” Int J Prod Econ, vol. 127, no. 2, pp. 262–277, Oct. 2010, doi: 10.1016/j.ijpe.2009.08.008.

P. Pontrandolfo, A. Gosavi, O. G. Okogbaa, and T. K. Das, “Global supply chain management: A reinforcement learning approach,” Int J Prod Res, vol. 40, no. 6, pp. 1299–1317, Apr. 2002, doi: 10.1080/00207540110118640.

R. Manzini, R. Accorsi, and M. Bortolini, “Operational planning models for distribution networks,” Int J Prod Res, vol. 52, no. 1, pp. 89–116, 2014, doi: 10.1080/00207543.2013.828168.

V. Kumar, N. Mishra, F. T. S. Chan, and A. Verma, “Managing warehousing in an agile supply chain environment: An F-AIS algorithm based approach,” Int J Prod Res, vol. 49, no. 21, pp. 6407–6426, 2011, doi: 10.1080/00207543.2010.528057.

V. N. S. A. Kumar, V. Kumar, M. Brady, J. A. Garza-Reyes, and M. Simpson, “Resolving forward-reverse logistics multi-period model using evolutionary algorithms,” Int J Prod Econ, vol. 183, pp. 458–469, Jan. 2017, doi: 10.1016/j.ijpe.2016.04.026.

A. D. Ganesh and P. Kalpana, “Future of artificial intelligence and its influence on supply chain risk management–A systematic review,” Computers & Industrial Engineering, vol. 169, p. 108206, 2022.

Y. Li, M. R. Kramer, A. J. M. Beulens, and J. G. A. J. van der Vorst, “A framework for early warning and proactive control systems in food supply chain networks,” Comput Ind, vol. 61, no. 9, pp. 852–862, Dec. 2010, doi: 10.1016/j.compind.2010.07.010.

A. Belhadi, S. Kamble, S. Fosso Wamba, and M. M. Queiroz, “Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework,” Int J Prod Res, vol. 60, no. 14, pp. 4487-4507, 2022, doi: 10.1080/00207543.2021.1950935.

M. A. Villegas and D. J. Pedregal, “Supply chain decision support systems based on a novel hierarchical forecasting approach,” Decis Support Syst, vol. 114, pp. 29–36, Oct. 2018, doi: 10.1016/j.dss.2018.08.003.

S. Papagiannidis, E. W. K. See-To, D. G. Assimakopoulos, and Y. Yang, “Identifying industrial clusters with a novel big-data methodology: Are SIC codes (not) fit for purpose in the Internet age?,” Comput Oper Res, vol. 98, pp. 355–366, Oct. 2018, doi: 10.1016/j.cor.2017.06.010.

H. Ma, Y. Wang, and K. Wang, “Automatic detection of false positive RFID readings using machine learning algorithms,” Expert Syst Appl, vol. 91, pp. 442–451, Jan. 2018, doi: 10.1016/j.eswa.2017.09.021.

P. Holimchayachotikul, R. Derrouiche, D. Damand, and K. Leksakul, “Value creation through collaborative supply chain: Holistic performance enhancement road map,” Production Planning and Control, vol. 25, no. 11, pp. 912–922, Aug. 2014, doi: 10.1080/09537287.2013.780313.

D. Simchi-Levi and M. X. Wu, “Powering retailers’ digitization through analytics and automation,” Int J Prod Res, vol. 56, no. 1–2, pp. 809–816, Jan. 2018, doi: 10.1080/00207543.2017.1404161.

N. K. Dev, R. Shankar, A. Gunasekaran, and L. S. Thakur, “A hybrid adaptive decision system for supply chain reconfiguration,” Int J Prod Res, vol. 54, no. 23, pp. 7100–7114, Dec. 2016, doi: 10.1080/00207543.2015.1134842.

X. Zhang et al., “An intelligent physarum solver for supply chain network design under profit maximization and oligopolistic competition,” Int J Prod Res, vol. 55, no. 1, pp. 244–263, Jan. 2017, doi: 10.1080/00207543.2016.1203075.

S. Zhang, C. K. M. Lee, K. Wu, and K. L. Choy, “Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels,” Expert Syst Appl, vol. 65, pp. 87–99, Dec. 2016, doi: 10.1016/j.eswa.2016.08.037.

L. A. Moncayo-Martínez and E. Mastrocinque, “A multi-objective intelligent water drop algorithm to minimise cost Of goods sold and time to market in logistics networks,” Expert Syst Appl, vol. 64, pp. 455–466, Dec. 2016, doi: 10.1016/j.eswa.2016.08.003.

S. L. Ting, Y. K. Tse, G. T. S. Ho, S. H. Chung, and G. Pang, “Mining logistics data to assure the quality in a sustainable food supply chain: A case in the red wine industry,” Int J Prod Econ, vol. 152, pp. 200–209, 2014, doi: 10.1016/j.ijpe.2013.12.010.

Z. Miao, S. Cai, and D. Xu, “Applying an adaptive tabu search algorithm to optimize truck-dock assignment in the crossdock management system,” Expert Syst Appl, vol. 41, no. 1, pp. 16–22, 2014, doi: 10.1016/j.eswa.2013.07.007.

M. G. Marchetta, F. Mayer, and R. Q. Forradellas, “A reference framework following a proactive approach for Product Lifecycle Management,” Comput Ind, vol. 62, no. 7, pp. 672–683, Sep. 2011, doi: 10.1016/j.compind.2011.04.004.

S. Moussawi, M. Koufaris, and R. Benbunan-Fich, “How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents,” Electronic Markets, vol. 31, no. 2, pp. 343-364, 2021.

Á. Rubio-Largo, M. A. Vega-Rodríguez, J. A. Gómez-Pulido, and J. M. Sánchez-Pérez, “A comparative study on multiobjective swarm intelligence for the routing and wavelength assignment problem,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 42, no. 6, pp. 1644–1655, 2012, doi: 10.1109/TSMCC.2012.2212704.

Y. Fu, M. Ding Prof., C. Zhou Prof., and H. Hu, “Route planning for unmanned aerial vehicle (UAV) on the sea using hybrid differential evolution and quantum-behaved particle swarm optimization,” IEEE Trans Syst Man Cybern Syst, vol. 43, no. 6, pp. 1451–1465, 2013, doi: 10.1109/TSMC.2013.2248146.

X. Wang, T. M. Choi, H. Liu, and X. Yue, “A novel hybrid ant colony optimization algorithm for emergency transportation problems during post-disaster scenarios,” IEEE Trans Syst Man Cybern Syst, vol. 48, no. 4, pp. 545–556, Apr. 2018, doi: 10.1109/TSMC.2016.2606440.

S. Xu, Y. Liu, and M. Chen, “Optimisation of partial collaborative transportation scheduling in supply chain management with 3PL using ACO,” Expert Syst Appl, vol. 71, pp. 173–191, Apr. 2017, doi: 10.1016/j.eswa.2016.11.016.

C. K. M. Lee, W. Ho, G. T. S. Ho, and H. C. W. Lau, “Design and development of logistics workflow systems for demand management with RFID,” Expert Syst Appl, vol. 38, no. 5, pp. 5428–5437, May 2011, doi: 10.1016/j.eswa.2010.10.012.

M. Klumpp, “Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements,” International Journal of Logistics Research and Applications, vol. 21, no. 3, pp. 224–242, May 2018, doi: 10.1080/13675567.2017.1384451.

V. Arora, F. T. S. Chan, and M. K. Tiwari, “An integrated approach for logistic and vendor managed inventory in supply chain,” Expert Syst Appl, vol. 37, no. 1, pp. 39–44, 2010, doi: 10.1016/j.eswa.2009.05.016.

D. Werthmann, D. Brandwein, C. Ruthenbeck, B. Scholz-Reiter, and M. Freitag, “Towards a standardised information exchange within finished vehicle logistics based on RFID and EPCIS,” Int J Prod Res, vol. 55, no. 14, pp. 4136–4152, Jul. 2017, doi: 10.1080/00207543.2016.1254354.

C. Dirican, “The Impacts of Robotics, Artificial Intelligence On Business and Economics,” Procedia Soc Behav Sci, vol. 195, pp. 564–573, Jul. 2015, doi: 10.1016/j.sbspro.2015.06.134.

D. Kannan, “Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process,” Int J Prod Econ, vol. 195, pp. 391–418, Jan. 2018, doi: 10.1016/j.ijpe.2017.02.020.




DOI: https://doi.org/10.18196/jrc.v5i5.21859

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Mohamed Kriouich, Hicham Sarir, Soulaiman Louah

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

Journal of Robotics and Control (JRC)

P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com

Kuliah Teknik Elektro Terbaik