隨著環保意識的 抬頭與逆物流之興起， 抬頭與逆物流之興起， 製造商被迫在產品的壽命結束時收回 製造商被迫在產品的壽命結束時收回 其產品 ，並思考如何使其產品零 件得以再利用、製造期能夠延長材料及，並思考如何使其產品零 件得以再利用、製造期能夠延長材料及，並思考如何使其產品零 件得以再利用、製造期能夠延長材料及，並思考如何使其產品零 件得以再利用、製造期能夠延長材料及，並思考如何使其產品零 件得以再利用、製造期能夠延長材料及，並思考如何使其產品零 件得以再利用、製造期能夠延長材料及，並思考如何使其產品零 件得以再利用、製造期能夠延長材料及，並思考如何使其產品零 件得以再利用、製造期能夠延長材料及件的壽命並減少報廢品處理量，然而拆卸是產回收最重要第一步。
拆卸線生產平衡問題 拆卸線生產平衡問題 (Disassembly Line Balancing Problem, DLBP)為對 拆 卸線的所有工作進行分配，使其均衡化並調整各 線的所有工作進行分配，使其均衡化並調整各 線的所有工作進行分配，使其均衡化並調整各 工作 站別的 工作負荷，常見站別的 工作負荷，常見站別的 工作負荷，常見最 佳化 拆卸線生產平衡指標有站別數量、危險零件週期時間以及工作負荷， 拆卸線生產平衡指標有站別數量、危險零件週期時間以及工作負荷， 拆卸線生產平衡指標有站別數量、危險零件週期時間以及工作負荷， 拆卸線生產平衡指標有站別數量、危險零件週期時間以及工作負荷， 拆卸線生產平衡指標有站別數量、危險零件週期時間以及工作負荷， 拆卸線生產平衡指標有站別數量、危險零件週期時間以及工作負荷， 因此本研究透過考慮資源限制的拆卸線問題，提出了一個數學模型並使用改良 此本研究透過考慮資源限制的拆卸線問題，提出了一個數學模型並使用改良 此本研究透過考慮資源限制的拆卸線問題，提出了一個數學模型並使用改良 此本研究透過考慮資源限制的拆卸線問題，提出了一個數學模型並使用改良 此本研究透過考慮資源限制的拆卸線問題，提出了一個數學模型並使用改良 後的 AND / OR圖（ TAOG）作為業之間的優先關係圖 ）作為業之間的優先關係圖 。本研究 在確定的週期 時間下， 提出了 三階段 啟發式適應性 啟發式適應性 啟發式適應性 啟發式適應性 啟發式適應性 啟發式適應性 基因演 算法 (Adaptive Genetic Algorithm, AGA)求解拆卸線 求解拆卸線 生產線平衡問題 生產線平衡問題 生產線平衡問題 生產線平衡問題 ，並減少 人員使用量 人員使用量 。於實驗結果表示 。於實驗結果表示 。於實驗結果表示 ，該方 法優於現有的中，大規模 法優於現有的中，大規模 法優於現有的中，大規模 拆卸 線生產線 平衡問題的 解決 方法 ，並 能有效提高 拆卸 線生產之 效率 。

Due to increasing environmental concerns, manufacturers are forced to take back their products at the end of products’ useful functional life. Manufacturers need to arrange how to recover product components and subassemblies for reuse, remanufacture, and recycle to extend the life of materials in use and reduce the disposal volume. However, disassembly is the first essential step on product recovery. The disassembly line balancing problem (DLBP) is the process of allocating a set of disassembly tasks to an ordered sequence of workstations in such a way that optimizes performance (e.g., number of stations, hazardous components number, cycle time and work load). Therefore, in this study, a mathematical model is presented for the DLBP by considering resource and labor constraints. Utilizing a transformed AND/OR Graph (TAOG) as the main input is to ensure the feasibility of the precedence relations among the tasks. The objective of this model is to minimize the number of labors used under determined cycle time. This research proposed a three-phase heuristic adaptive genetic algorithm (AGA) to optimize the labors number in the disassembly line. The experimental results indicate that the proposed method is superior to the existing approaches for medium and large scale in DLBPs.

摘要 ......................................................... I
Abstract .................................................... II
致謝 ........................................................ III
Contents .................................................... IV
List of Tables .............................................. VI
List of Figures ............................................ VIII
Chapter 1: Introduction ..................................... 1
1.1 Background .............................................. 1
1.2 Objectives .............................................. 4
1.3 Research Method ......................................... 5
1.4 Organization of Thesis .................................. 6
Chapter 2: Literature Review ................................ 7
2.1 Disassembly Line Balancing Problem (DLBP) ............... 7
2.2 Disassembly Relations and Diagram ....................... 13
2.3 Genetic Algorithm (GA) .................................. 14
Chapter 3: Problem Definition ............................... 17
3.1 Characteristics of Transformed AND/OR Graph (TAOG) ...... 17
3.2 Problem Statement ....................................... 20
3.3 Notations and Assumptions ............................... 23
3.4 Problem Formulation ..................................... 25
Chapter 4: Methodology ...................................... 32
4.1 DLBP Solution Module .................................... 32
4.2 Three-Phase Heuristic Adaptive Genetic Algorithms (AGA).. 33
4.2.1 Phase 1: Derive the Disassembly Path from TAOG .........34
4.2.2 Phase 2: Priority Rule-Based Method (PRBM) ......... .. 35
4.2.3 Phase 3: Heuristic AGA ................................ 36
4.2.3.1 Initial Population .................................. 37
4.2.3.2 Decoding ............................................ 37
4.2.3.3 Evaluation of Fitness Value ......................... 43
4.2.3.4 Generation Replacement .............................. 43
4.2.3.5 Selection ........................................... 44
4.2.3.6 Crossover ........................................... 45
4.2.3.7 Mutation ............................................ 48
4.3 Response Surface Methodology (RSM)....................... 49
4.4 Response Surface Design ................................. 52
4.4.1 Central Composite Circumscribed (CCC) Design .......... 53
4.4.2 Central Composite Inscribed (CCI) Design .............. 53
4.4.3 Central Composite Face-Centered (CCF) Design........... 54
Chapter 5: Computational Study .............................. 55
5.1 Illustrated Example ..................................... 55
5.2 Experimental Design Case ................................ 63
5.3 AGA Parameter Setting ................................... 67
5.3.1 Result of AGA Parameter Setting for Simple Case ....... 71
5.3.2 Substitute the Best AGA Parameter into Complex Case ... 76
5.4 Influence of System Parameters Setting .................. 79
Chapter 6: Conclusion ....................................... 85
Reference ................................................... 87
Appendix .................................................... 95

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