利用柔性運算法針對產品拆解排序問題進行求解

林相宇

doi:10.6843/NTHU.2009.00097

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利用柔性運算法針對產品拆解排序問題進行求解

A Soft Computing Algorithm for Disassembly Sequencing

林相宇 , Masters　　Advisor：葉維彰　　

英文 DOI： 10.6843/NTHU.2009.00097

拆解；間斷型例子群演算法；田口方法； Disassembly ； Discrete particle swarm optimization (DPSO) ； Taguchi methods

Abstract │ Reference (35) │ Times Cited (4) │ Altmetrics

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In recent years, it is an important issue to handle products at the end-of-life (EOL). In order to efficiently recover the EOL products, it is necessary to disassemble the product into several smaller components in this process. However, the disassembly time is a performance index which depends on the disassembly sequence of components. The purpose of the study is to obtain the optimal disassembly sequence of the product which can minimize the disassembly time. In this article, we propose a soft computing algorithm to solve this kind of problem by combining discrete particle swarm optimization (DPSO) with concept of precedence preservative crossover (PPX) in genetic algorithm. Discrete particle swarm optimization (DPSO) is a population based stochastic optimization technique that is used to solve problems in discrete type, and PPX guarantees feasible solution at each of evaluations. In order to have a better result, we apply Taguchi methods to design experiment that was used to obtain the optimization of the parameters of DPSO. Finally, two benchmark problems will show the efficiency of this approach.

Reference ( 35 ) 〈TOP〉

[3] D. Navin-Chandra, “The recovery problem in product design” ,Journal of Engineering Design, Vol. 5, 1994, pp. 65–86.
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[4] J. Kennedy, R. C. Eberhart, “Particle swarm optimization” , In Proc.
連結：
IEEE Int. Conf. Neural Networks, Piscataway, November 1995, pp. 1942–1948.
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[5] J. Kennedy, R. C. Eberhart, “A discrete binary version of the particle swarm algorithm” , In Proc. Of IEEE Int. Conf. on Systems, Man and Cybernetics, Vol. 5, No. 12-15, 1997, pp.4104-4108.
連結：
[7] K.-K. Seo, J.-H. Park, and D.-S. Jang, “Optimal disassembly sequence using genetic algorithms considering economic and environmental aspects” , The International Journal of Advanced Manufacturing Technology, Vol. 18, No. 5, 2001, pp. 371–380.
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