A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem - A case study on supply chain model

被引：131
|
作者：
Kuo, R. J. [1 ]
Han, Y. S. [2 ]
机构：
[1] Natl Taiwan Univ Sci & Technol, Dept Ind Management, Taipei, Taiwan
[2] Taiwan Semicond Mfg Co, Dept Mfg, Hsinchu, Taiwan
来源：
APPLIED MATHEMATICAL MODELLING | 2011年 / 35卷 / 08期
关键词：
Bi-level linear programming; Supply chain management; Genetic algorithm; Particle swarm optimization; NEURAL-NETWORK APPROACH; PARAMETERS; SEARCH;
D O I：
10.1016/j.apm.2011.02.008
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The main goal of supply chain management is to coordinate and collaborate the supply chain partners seamlessly. On the other hand, bi-level linear programming is a technique for modeling decentralized decision. It consists of the upper level and lower level objectives. Thus, this paper intends to apply bi-level linear programming to supply chain distribution problem and develop an efficient method based on hybrid of genetic algorithm (GA) and particle swarm optimization (PSO). The performance of the proposed method is ascertained by comparing the results with GA and PSO using four problems in the literature and a supply chain distribution model. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码：3905 / 3917
页数：13
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