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Application of adaptive strategy for supply chain agent

Author

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  • Yoon Sang Lee

    (Columbus State University)

  • Riyaz Sikora

    (University of Texas at Arlington)

Abstract

With the tremendous increase in the globalization of trade the corresponding supply chains supporting the manufacture, distribution and supply of goods has become extremely complex. Intelligent agents can help with the problem of effective management of these complex supply chains. In this paper we introduce the design, implementation and testing of an intelligent agent for handling procurement, customer sales, and scheduling of production in a stylized supply chain environment. The supply chain environment used in this paper is modeled after the trading agent competition that is held annually to choose the best agent for managing a supply chain. Our supply chain agent, which we call SCMaster, uses dynamic inventory control and various reinforcement learning techniques like Q-learning, Softmax, ε-greedy, and sliding window protocol to make our agent adapt dynamically to the changing environment created by competing agents. A multi-agent simulation environment is developed in Java to test the efficacy of our agent design. Two competing agents are created modeled after the winners of past trading agent competitions and are tested against our agent in various experimental designs. Results of simulations show that our agent has better performance compared to the other agents.

Suggested Citation

  • Yoon Sang Lee & Riyaz Sikora, 2019. "Application of adaptive strategy for supply chain agent," Information Systems and e-Business Management, Springer, vol. 17(1), pages 117-157, March.
    • Handle: RePEc:spr:infsem:v:17:y:2019:i:1:d:10.1007_s10257-018-0378-y
    DOI: 10.1007/s10257-018-0378-y
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    References listed on IDEAS

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    3. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    4. Soheil Sibdari & Xiaoqin Shelley Zhang & Saban Singh, 2012. "A dynamic programming approach for agent's bidding strategy in TAC-SCM game," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 14(2), pages 121-134.
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    Cited by:

    1. 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).
    2. Lechtenberg, Sandra & Hellingrath, Bernd, 2021. "Applications of artificial intelligence in supply chain management: Identification of main research fields and greatest industry interests," ERCIS Working Papers 37, University of Münster, European Research Center for Information Systems (ERCIS).

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