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

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 ap...

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Veröffentlicht in:Applied mathematical modelling Jg. 35; H. 8; S. 3905 - 3917
Hauptverfasser: Kuo, R.J., Han, Y.S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Inc 01.08.2011
Elsevier
Schlagworte:
Applied sciences
Bi-level linear programming
Exact sciences and technology
Genetic algorithm
Genetic algorithms
Linear programming
Logistics
Management
Mathematical models
Operational research and scientific management
Operational research. Management science
Optimization
Particle swarm optimization
Supply chain management
Supply chains
Supply chain management
Bi-level linear programming
Genetic algorithm
Particle swarm optimization
Evolutionary algorithm
Bilevel programming
Swarm intelligence
Modeling
Logistics
ISSN:0307-904X
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-2
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ISSN:0307-904X
DOI:10.1016/j.apm.2011.02.008