A modified Pareto genetic algorithm for multi-objective build-to-order supply chain planning with product assembly

The build-to-order supply chain (BOSC) model is a key operation model for providing services/products at present. This study focuses on performing the supply chain planning for the BOSC network. The planning is designed to integrate supplier selection, product assembly, as well as the logistic distr...

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Veröffentlicht in:Advances in engineering software (1992) Jg. 41; H. 7; S. 1011 - 1022
Hauptverfasser: Che, Z.H., Chiang, C.J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 2010
Schlagworte:
Assembly
Build-to-order supply chain
Construction
Genetic algorithms
Logistic distribution system
Markets
Mathematical models
Optimization
Pareto genetic algorithm
Pareto optimality
Product assembly
Supplier selection
Suppliers
Supply chains
Build-to-order supply chain
Supplier selection
Logistic distribution system
Product assembly
Pareto genetic algorithm
ISSN:0965-9978
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Zusammenfassung:The build-to-order supply chain (BOSC) model is a key operation model for providing services/products at present. This study focuses on performing the supply chain planning for the BOSC network. The planning is designed to integrate supplier selection, product assembly, as well as the logistic distribution system of the supply chain in order to meet market demands. With multiple suppliers and multiple customer needs, the assembly model can be divided into several sub-assembly steps by applicable sequence. Considering three evaluation criteria, namely costs, delivery time, and quality, a multi-objective optimization mathematical model is established for the BOSC planning in this study. The multi-objective problems usually have no unique optimal solution, and the Pareto genetic algorithm (PaGA) can find good trade-offs among all the objectives. Therefore, the PaGA is applied to find solutions for the mathematical model. In addition, regarding BOSC problems solving, this study proposes a modified Pareto genetic algorithm (mPaGA) to improve the solution quality through revision of crossover and mutation operations. After application and analysis of cases, mPaGA is found to be superior to traditional PaGA (tPaGA) in solution performance.
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ISSN:0965-9978
DOI:10.1016/j.advengsoft.2010.04.001