Application of adaptive strategy for supply chain agent

Yoon Sang Lee () and Riyaz Sikora ()
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Yoon Sang Lee: Columbus State University
Riyaz Sikora: University of Texas at Arlington

Information Systems and e-Business Management, 2019, vol. 17, issue 1, No 5, 117-157

Abstract: 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.

Keywords: Multi-agent systems; Artificial intelligence; E-commerce; Simulation; Supply chain management (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10257-018-0378-y

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