Volume 9 Number 9 (Sep. 2014)
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JSW 2014 Vol.9(9): 2335-2341 ISSN: 1796-217X
doi: 10.4304/jsw.9.9.2335-2341

Modeling Supply Chain Facility Location Problem and Its Solution Using a Genetic Algorithm

Bing Wang1, Xiaokang Fu2, Tinggui Chen3, Guanglan Zhou4
1Hangzhou College of Commerce, Zhejiang Gongshang University, Hangzhou, P. R. China
2School of Business Administration, Zhejiang Gongshang University, Hangzhou, P. R. China
3College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou, P. R. China
4Center for Studies of Modern Business, Zhejiang Gongshang University, Hangzhou, P. R. China


Abstract—The supply chain facility location problem is an important foundation in the supply chain. The quality of facilities location directly influences the production and operation status of enterprise. Therefore, it is critical for enterprises to adopt scientific and effective methods for facility location problem assessment and a smooth decision. This article concentrates on the development status of location problem, focusing on the linear programming model and a genetic algorithm in the location problem analysis and analytical method. In the linear programming model, because the given complex table calculation method is too complicated and the workload is very large, the Excel software is proposed to solve the location problem, which can greatly improve the efficiency of enterprise facility location problem. In addition, a genetic algorithm based on MATLAB toolbox is applied to another type of facility location problem, which provides a referential method for location decision under different conditions and different facilities.

Index Terms—supply chain facility location problem, linear programming, EXCEL, MATLAB, genetic algorithm

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Cite: Bing Wang, Xiaokang Fu, Tinggui Chen, Guanglan Zhou, "Modeling Supply Chain Facility Location Problem and Its Solution Using a Genetic Algorithm," Journal of Software vol. 9, no. 9, pp. 2335-2341, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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