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Managing Information Complexity of Supply Chains via Agent-Based Genetic Programming

  • Ken Taniguchi
  • Setsuya Kurahashi
  • Takao Terano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2417)

Abstract

This paper proposes agent-based formulation of a Supply Chain Management (SCM) system for manufacturing firms. We model each firm as an intelligent agent, which communicates each other through the blackboard architecture in distributed artificial intelligence. To cope with the issues of conventional SCM systems, we employ the concept of information entropy, which represents the complexity of the purchase, sales, and inventory activities of each firm. Based on the idea, we implement an agent-based simulator to learn ‘good’ decisions via genetic programming in a logic programming environment. From intensive experiments, our simulator have shown good performance against the dynamic environmental changes.

Keywords

Supply Chain Management Inventory Activity Intelligent Agent Information Entropy Manufacturing Firm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    S. Sivadasan, J. Efstathiou, R. Shirazi, J. Alves, G. Frizelle, A.Calinescu: Information Complexity as a Determining Factor in the Evolution of Supply Chains, International Workshop on Emergent Synthesis-IWES’99, pp.237–242, 1999.Google Scholar
  2. 2.
    Ken Taniguchi, Setsuya Kurahashi and Takao Terano: Managing Information Complexity in a Supply Chain Model by Agent-Based Genetic Programming, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001) Late Breaking Papers, pp.413–420, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ken Taniguchi
    • 1
  • Setsuya Kurahashi
    • 1
  • Takao Terano
    • 1
  1. 1.Graduate School of Systems ManagementUniversity of TsukubaTokyoJapan

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