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Modeling of Biological Intelligence for SCM System Optimization
Joint Authors
Wang, Wanliang
Zheng, Yujun
Cattani, Carlo
Chen, Sheng-yong
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-11-24
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Chen, Sheng-yong& Zheng, Yujun& Cattani, Carlo& Wang, Wanliang. 2011. Modeling of Biological Intelligence for SCM System Optimization. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-497365
Modern Language Association (MLA)
Chen, Sheng-yong…[et al.]. Modeling of Biological Intelligence for SCM System Optimization. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-10.
https://search.emarefa.net/detail/BIM-497365
American Medical Association (AMA)
Chen, Sheng-yong& Zheng, Yujun& Cattani, Carlo& Wang, Wanliang. Modeling of Biological Intelligence for SCM System Optimization. Computational and Mathematical Methods in Medicine. 2011. Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-497365
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-497365