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.
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References
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.
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.
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© 2002 Springer-Verlag Berlin Heidelberg
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Taniguchi, K., Kurahashi, S., Terano, T. (2002). Managing Information Complexity of Supply Chains via Agent-Based Genetic Programming. In: Ishizuka, M., Sattar, A. (eds) PRICAI 2002: Trends in Artificial Intelligence. PRICAI 2002. Lecture Notes in Computer Science(), vol 2417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45683-X_67
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DOI: https://doi.org/10.1007/3-540-45683-X_67
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