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Insights on Multi-Agent Systems Applications for Supply Chain Management

Author

Listed:
  • Roberto Dominguez

    (Industrial Management & Business Administration Department, University of Seville, 41004 Seville, Spain)

  • Salvatore Cannella

    (Department of Civil Engineering and Architecture (DICAR), University of Catania, 95131 Catania, Italy)

Abstract

In this paper, we review relevant literature on the development of multi-agent systems applications for supply chain management. We give a general picture of the state of the art, showing the main applications developed using this novel methodology for analyzing diverse problems in industry. We also analyze generic frameworks for supply chain modelling, showing their main characteristics. We discuss the main topics addressed with this technique and the degree of development of the contributions.

Suggested Citation

  • Roberto Dominguez & Salvatore Cannella, 2020. "Insights on Multi-Agent Systems Applications for Supply Chain Management," Sustainability, MDPI, vol. 12(5), pages 1-13, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1935-:d:327997
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    References listed on IDEAS

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    1. Jingsi Huang & Jie Song, 2018. "Optimal inventory control with sequential online auction in agriculture supply chain: an agent-based simulation optimisation approach," International Journal of Production Research, Taylor & Francis Journals, vol. 56(6), pages 2322-2338, March.
    2. Perona, Marco & Miragliotta, Giovanni, 2004. "Complexity management and supply chain performance assessment. A field study and a conceptual framework," International Journal of Production Economics, Elsevier, vol. 90(1), pages 103-115, July.
    3. Mohammed Dahane & M’hammed Sahnoun & Belgacem Bettayeb & David Baudry & Hamza Boudhar, 2017. "Impact of spare parts remanufacturing on the operation and maintenance performance of offshore wind turbines: a multi-agent approach," Journal of Intelligent Manufacturing, Springer, vol. 28(7), pages 1531-1549, October.
    4. Troy J Strader & Fu-ren Lin & Michael J Shaw, 1998. "Simulation of Order Fulfillment in Divergent Assembly Supply Chains," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(2), pages 1-5.
    5. Utomo, Dhanan Sarwo & Onggo, Bhakti Stephan & Eldridge, Stephen, 2018. "Applications of agent-based modelling and simulation in the agri-food supply chains," European Journal of Operational Research, Elsevier, vol. 269(3), pages 794-805.
    6. Hou, Yunzhang & Wang, Xiaoling & Wu, Yenchun Jim & He, Peixu, 2018. "How does the trust affect the topology of supply chain network and its resilience? An agent-based approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 229-241.
    7. Caroline Krejci & Benita Beamon, 2015. "Impacts of Farmer Coordination Decisions on Food Supply Chain Structure," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-19.
    8. Kleijnen, J.P.C., 2005. "Supply chain simulation tools and techniques : A survey," Other publications TiSEM d0050225-57bd-4114-ab15-7, Tilburg University, School of Economics and Management.
    9. Sivadasan, S. & Efstathiou, J. & Calinescu, A. & Huatuco, L. Huaccho, 2006. "Advances on measuring the operational complexity of supplier-customer systems," European Journal of Operational Research, Elsevier, vol. 171(1), pages 208-226, May.
    10. Kaihara, Toshiya, 2003. "Multi-agent based supply chain modelling with dynamic environment," International Journal of Production Economics, Elsevier, vol. 85(2), pages 263-269, August.
    11. Dominguez, Roberto & Cannella, Salvatore & Barbosa-Póvoa, Ana P. & Framinan, Jose M., 2018. "Information sharing in supply chains with heterogeneous retailers," Omega, Elsevier, vol. 79(C), pages 116-132.
    12. Zhen Li & Hongming Zhu & Qingfeng Meng & Changzhi Wu & Jianguo Du, 2019. "Manufacturers’ Green Decision Evolution Based on Multi-Agent Modeling," Complexity, Hindawi, vol. 2019, pages 1-14, April.
    13. Ghadimi, Pezhman & Ghassemi Toosi, Farshad & Heavey, Cathal, 2018. "A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain," European Journal of Operational Research, Elsevier, vol. 269(1), pages 286-301.
    14. Chatfield, Dean C. & Harrison, Terry P. & Hayya, Jack C., 2009. "SCML: An information framework to support supply chain modeling," European Journal of Operational Research, Elsevier, vol. 196(2), pages 651-660, July.
    15. Chatfield, Dean C. & Pritchard, Alan M., 2013. "Returns and the bullwhip effect," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 159-175.
    16. Chatfield, Dean C. & Pritchard, Alan M., 2018. "Crossover aware base stock decisions for service-driven systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 312-330.
    17. Salvatore Cannella & Carmela Di Mauro & Roberto Dominguez & Alessandro Ancarani & Florian Schupp, 2019. "An exploratory study of risk aversion in supply chain dynamics via human experiment and agent-based simulation," International Journal of Production Research, Taylor & Francis Journals, vol. 57(4), pages 985-999, February.
    18. Li, Gang & Yang, Hongjiao & Sun, Linyan & Ji, Ping & Feng, Lei, 2010. "The evolutionary complexity of complex adaptive supply networks: A simulation and case study," International Journal of Production Economics, Elsevier, vol. 124(2), pages 310-330, April.
    19. Danping Wen & Jing Li & Tiaojun Xiao, 2019. "Impact of quality regulation policy on performance of a remanufacturing supply chain with non-waste returns," International Journal of Production Research, Taylor & Francis Journals, vol. 57(11), pages 3678-3694, June.
    20. Nagesh Shukla & Senevi Kiridena, 2016. "A fuzzy rough sets-based multi-agent analytics framework for dynamic supply chain configuration," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 6984-6996, December.
    21. Ledwoch, Anna & Yasarcan, Hakan & Brintrup, Alexandra, 2018. "The moderating impact of supply network topology on the effectiveness of risk management," International Journal of Production Economics, Elsevier, vol. 197(C), pages 13-26.
    22. Dominguez, Roberto & Cannella, Salvatore & Barbosa-Póvoa, Ana P. & Framinan, Jose M., 2018. "OVAP: A strategy to implement partial information sharing among supply chain retailers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 122-136.
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    3. Xu, Liming & Mak, Stephen & Brintrup, Alexandra, 2021. "Will bots take over the supply chain? Revisiting agent-based supply chain automation," International Journal of Production Economics, Elsevier, vol. 241(C).

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