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(Hsiang-Yu Lin)

指導教授 ： 葉維彰

國立清華大學/工學院/工業工程與工程管理學系/碩士(2009年)

https://doi.org/10.6843/NTHU.2009.00097

摘要

近幾年來，如何處理在產品生命週期末期的產品儼然成為一個重要的課題。 為了能更有效率回收這些產品，適當的拆解這些產品使其成為更小的元件是必須要的動作。然而，拆解的時間常常取決於針對元件拆解的順序。因此，本研究的目的在於配置最適合的元件拆解順序以期達到最小拆解時間。由於此問題為間斷型的整數規劃問題，在這裡我們提出一個間斷型粒子群演算法並結合優先保留交配的觀念來解此間斷型問題。優先保留交配法最初是使用於基因演算法，這裡藉由結合優先保留交配法能避免粒子群演算法的解落入不可行解。為了使演算法能有較高的搜尋效率，我們引用了田口方法來針對演算法中的參數進行最佳化的設計。在本文中，將會提出兩個標竿問題並藉由此間斷型粒子群演算法來求得最佳解。

關鍵字

拆解 ； 間斷型例子群演算法 ； 田口方法

並列摘要

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.

並列關鍵字

Disassembly ； Discrete particle swarm optimization (DPSO) ； Taguchi methods

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