EconPapers    
Economics at your fingertips  
 

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

Ken Taniguchi, Setsuya Kurahashi, Takao Terano

No 238, Computing in Economics and Finance 2001 from Society for Computational Economics

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.

Keywords: Supply Chain; Genetic Programming; Logic Programming (search for similar items in EconPapers)
JEL-codes: C88 (search for similar items in EconPapers)
Date: 2001-04-01
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf1:238

Access Statistics for this paper

More papers in Computing in Economics and Finance 2001 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().

 
Page updated 2024-06-27
Handle: RePEc:sce:scecf1:238