利用基因演算法求解考慮隨機零件相關學習效果之拆解順序問題

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研究生：	林甄敏 Lin, Chen-Min
論文名稱：	利用基因演算法求解考慮隨機零件相關學習效果之拆解順序問題 Optimize Disassembly Sequencing Problem with Stochastic Job-dependent Learning Effects Using Genetic Algorithm
指導教授：	葉維彰 Yeh, Wei-Chang
口試委員:	桑慧敏 Song, Whey-Ming 賴鵬仁 Lai, Peng-Jen
學位類別：	碩士 Master
系所名稱：	工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management
論文出版年：	2012
畢業學年度：	100
語文別：	英文
論文頁數：	52
中文關鍵詞：	終止產品、拆解順序問題、學習效果、基因演算法
外文關鍵詞：	disassembly sequencing problem, Precedence Preservative Crossover, feasible solution generator
相關次數：	點閱：3 下載：0
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隨著環保意識的抬頭與各種環保相關法規的制定，許多企業對於終止產品 (end-of-life product; EOL product) 的回收與處理愈來愈重視。將產品由終止產品拆解為子裝配品或零件即為處理過程中一重要步驟。不同的產品拆解順序會影響產品拆解的效率，即拆解所需耗費的時間。因此，近年來拆解順序問題(disassembly sequencing problem; DSP) 逐漸成為重要研究議題。大部分過去對拆解問題的研究，皆假設各零件在不同位置的處理時間為確定且已知。本研究考慮了拆解順序不同所產生的學習效果，並考慮了學習率的隨機性與零件相依特性。拆解問題隨著零件數的增加，其求得最佳解的困難性也隨之倍增。由於拆解問題是一計算複雜度為NP-complete的問題，本研究採用基因演算法(genetic algorithm; GA)進行求解以最小化期望總拆解時間。本研究以六種終止產品為例進行模擬實驗分析，並粒子群演算法(particle swarm optimization; PSO) 進行比較，證明基因演算法對求解隨機零件相關拆解順序問題能找到使期望總總拆解時間與其變異較小的解且有較佳的收斂效果。接著針對基因演算法中，計算代數與染色體數兩參數之設定進行實驗並加以分析，找出使演算法更有效率之參數組合。

With eco-awareness and eco-regulation, the disposal of end-of-life (EOL) product has been considered. Disassembly is the operation to apart the component or subassembly from the main product. The disassembly sequence would affect the disassembly efficiency. So, disassembly sequencing problem (DSP) has become increasingly important in the process of recycling, reclamation, or remanufacturing EOL products. However, most of the studies in the disassembly sequencing plan assume that the processing time is deterministic for each component’s disassembling procedure. For a realistic and logical approach to the DSP, this thesis takes account of the learning effect with stochastic concept and job-dependent, that is the condition in which different components may have different learning rate. With the quantity of components increasing, the sequences of DSP would grow too dramatically to find the optimal solution. Considering the NP-complete nature of the stochastic problem, a genetic algorithm (GA) is applied to minimize the total expected disassembling time for this problem. Taking six EOL products as examples, an experiment is executed and the results are compared with another algorithm, particle swarm optimization (PSO). GA is verified with better effectiveness and convergence efficiency than PSO. Another experiment is executed to determine the best setting of iterative generation and chromosome numbers for making the GA more efficiency.

中文摘要        I
Abstract        II
致謝        III
Contents        IV
List of Figures    VI
List of Tables    VII
Chapter 1    Introduction    1
1    Background and Motivation    1
2    Research purpose    3
3    Research framework    3
Chapter 2    Literature Review    5
1    Disassembly sequencing problem    5
1.1    The initial of disassembly    5
1.2    Disassembly line balancing problem (DLBP)    6
1.3    Disassembly sequencing problem (DSP)    7
2    Learning effect    7
2.1    Learning effects in scheduling    8
2.2    Job-dependent position-based learning effects    9
3    Genetic algorithm    10
3.1    Genetic algorithm in sequencing problem    10
3.2    GA with precedence preservative crossover    12
4    Feasible solution generator    12
Chapter 3    Problem Statement    13
1    Notation    13
2    Assumption    15
3    Model formulation    16
Chapter 4    Research Method    20
1    Genetic algorithm    20
1.1    Parameter nomenclature    21
1.2    Chromosome    23
1.3    Initialization using feasible solution generator    23
1.4    Crossover    25
1.5    Mutation    27
1.6    Fitness evaluation    27
Chapter 5    Numerical Result    29
1    Situation interpretation    29
2    Parameter adjustment    32
2.1    Experiment interpretation    32
2.2    Experiment result    33
3    Numerical performance    41
3.1    Experiment interpretation    41
3.2    Experiment result    41
Chapter 6    Conclusion    45
1    Contribution    45
2    Future work    45
Reference        47

                                

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