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Managing Information Complexity in a Supply Chain Model by Agent-Based Genetic Programming

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  • Ken Taniguchi, Setsuya Kurahashi, Takao Terano

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 overcome 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.

Suggested Citation

  • Ken Taniguchi, Setsuya Kurahashi, Takao Terano, 2001. "Managing Information Complexity in a Supply Chain Model by Agent-Based Genetic Programming," Computing in Economics and Finance 2001 238, Society for Computational Economics.
  • Handle: RePEc:sce:scecf1:238
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    More about this item

    Keywords

    Supply Chain; Genetic Programming; Logic Programming;

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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