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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Vydáno v:	Applied mathematical modelling Ročník 35; číslo 8; s. 3905 - 3917
Hlavní autoři:	Kuo, R.J., Han, Y.S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Inc 01.08.2011 Elsevier
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Han, Y.S. Kuo, R.J.
Author_xml	– sequence: 1 givenname: R.J. surname: Kuo fullname: Kuo, R.J. email: rjkuo@mail.ntust.edu.tw organization: Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan – sequence: 2 givenname: Y.S. surname: Han fullname: Han, Y.S. organization: Department of Manufacturing, Taiwan Semiconductor Manufacturing Company, Hsinchu, Taiwan
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Keywords	Supply chain management Bi-level linear programming Genetic algorithm Particle swarm optimization Evolutionary algorithm Bilevel programming Swarm intelligence Modeling Logistics
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Snippet	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...
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SubjectTerms	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
Title	A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem – A case study on supply chain model
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