Managing Information Complexity in a Supply Chain Model by Agent-Based Genetic Programming
AbstractThis 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.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 238.
Date of creation: 01 Apr 2001
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Web page: http://www.econometricsociety.org/conference/SCE2001/SCE2001.html
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Supply Chain; Genetic Programming; Logic Programming;
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