Modeling of Biological Intelligence for SCM System Optimization

被引:46
作者
Chen, Shengyong [1 ]
Zheng, Yujun [1 ]
Cattani, Carlo [2 ]
Wang, Wanliang [1 ]
机构
[1] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China
[2] Univ Salerno, Dept Math, I-84084 Fisciano, Italy
基金
中国国家自然科学基金;
关键词
SUPPLY CHAIN NETWORK; PARTICLE SWARM OPTIMIZATION; ARTIFICIAL IMMUNE-SYSTEM; HYBRID GENETIC ALGORITHM; DIFFERENTIAL EVOLUTION; MULTIOBJECTIVE OPTIMIZATION; DISTRIBUTION CENTERS; ORDER DISTRIBUTION; LOCAL SEARCH; ALLOCATION;
D O I
10.1155/2012/769702
中图分类号
Q [生物科学];
学科分类号
090105 [作物生产系统与生态工程];
摘要
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.
引用
收藏
页数:10
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