Modeling of Biological Intelligence for SCM System Optimization
Author(s) -
Shengyong Chen,
YuJun Zheng,
Carlo Cattani,
Wanliang Wang
Publication year - 2011
Publication title -
computational and mathematical methods in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 48
eISSN - 1748-6718
pISSN - 1748-670X
DOI - 10.1155/2012/769702
Subject(s) - computer science , swarm intelligence , artificial immune system , differential evolution , evolutionary algorithm , computational intelligence , artificial intelligence , evolutionary programming , genetic algorithm , machine learning , particle swarm optimization
This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.
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