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

Authors:
Z. H. Che;C. J. Chiang
Affiliations:
Department of Industrial Engineering & Management, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 106, Taiwan, ROC;Department of Industrial Engineering & Management, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 106, Taiwan, ROC
Venue:
Advances in Engineering Software
Year:
2010

Citing 10
Cited 3

Genetic Algorithms and Manufacturing Systems Design

Genetic Algorithms and Manufacturing Systems Design
Vendor selection in outsourcing

Computers and Operations Research
A hybrid approach to supplier selection for the maintenance of a competitive supply chain

Expert Systems with Applications: An International Journal
A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor

Information Sciences: an International Journal
Parameter optimization for growth model of greenhouse crop using genetic algorithms

Applied Soft Computing
Parameter estimation in mathematical models using the real coded genetic algorithms

Expert Systems with Applications: An International Journal
A genetic algorithm approach for multi-objective optimization of supply chain networks

Computers and Industrial Engineering
The Williams and Otto Chemical Plant re-evaluated using a Pareto-optimal formulation aided by Genetic Algorithms

Applied Soft Computing
A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II

Computers and Industrial Engineering
Statistical analysis of the main parameters involved in the designof a genetic algorithm

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Multi-objective decision-making methodology to create an optimal design chain partner combination

Computers and Industrial Engineering
Multi-objective optimization of stochastic disassembly line balancing with station paralleling

Computers and Industrial Engineering
Pareto process optimization of product development project using bi-objective hybrid genetic algorithm

Advances in Engineering Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.