摘要
本研究的研究動機是來自於成衣產業所面對的生產計劃管理問題。本研究考慮了非等效平行機台的生產環境，並同時考慮工作之間存在順序及機台相依的整備時間。因為不同工作的生產時間窗有重疊的情況，管理者將會面臨困難的去規劃一個排程讓工作可以在交期日之前完成。再者，在此生產環境下，為了要準時達交顧客的訂單，決策者必須同時將一個工作分配到不同的生產線上加工。
本研究使用四個層級的程序來解決排程問題。第一個層級在訂單詢問階段使用優先式最早交期法 (preemptive earliest due date rule) 判斷產能是否足夠應付已接收的訂單。第二與第三個層級解決現場排程階段問題，本研究比較了四個方法，分別是張【2013】提出的混合整數規劃及三種演算法-模擬退火法、塔布搜尋法與遺傳演算法，這三種演算法結合整數規劃去解決第二與第三層級的排程問題。第二與第三層級的不同之處在於目標函式的不同。第二階段首要目標是最小化總延遲時間，而第三階段次要目標為最小化整備時間。第四個層級提出一個混合整數規劃去解決不同訂單有分顏色大小的問題。
實驗結果顯示，在第二與第三個層級塔布搜尋法的表現會優於其他方法。而在第四個層級，實驗結果顯示本研究提出的混整數模型可以找到有效率的排程。

關鍵字: 縫紉製程、成衣產業、非等效平行機台、順序相依整備時間、機台相依整備時間、工作可切割、模擬退火法、塔布搜尋法、基因演算法
 

Abstract
This study is motivated by the production management problem for apparel manufacturing. A complex production environment – unrelated parallel machines, sequence-dependent and machine-dependent setup time – is considered in this study. Due to the variation and overlap among the production time windows of different orders, it is difficult for the manufacturer to schedule the production activities in such a way that all confirmed orders are finished on time. Also, in such an environment, in order to complete an order with arbitrary ready date and due date on time, the factory may have to split the production of the order into several production lines and produce the order simultaneously.
Four phases procedure is adopted in this study to solve the scheduling problem. In phase 1, to deal with the capacity requirement planning problem at the order-entry stage, the preemptive earliest due date (PEDD) rule is used to check the capacity feasibility for a set of considered orders. In phases 2 and 3, to conduct order-level scheduling, this study compares four methods, mixed integer programming (MIP) proposed by Hung and Chang (2013) and the three search methods – simulated annealing (SA), tabu search (TS) and genetic algorithm (GA) – incorporated with linear programming models. The difference between phases 2 and 3 of order-level scheduling is their objective functions. In phase 2, a higher priority objective of minimizing total tardiness is adopted, while in phase 3, a lower priority objective of minimizing total setup times is used. In phase 4, to deal with the color-size level scheduling, an MIP model is proposed with the objective of minimizing total number of changeovers.
The computer experiment results show that TS outperforms other methods in phases 2 and 3. In phase 4, the proposed MIP model can efficiently and effectively solve the problems.
Keywords: sewing scheduling; apparel manufacturing; unrelated parallel machines; sequence-dependent setup time; machine-dependent setup time; job-splitting; simulated annealing; tabu search; genetic algorithm.

TABLE OF CONTENTS
LIST OF FIGURES VI
LIST OF TABLES VII
1. Introduction 1
1.1. Background 1
1.2. Two stages of production management 4
2. Literature Reviews 8
3. Approaches 11
3.1. Assumption and input parameters 13
3.2. Phase 1: capacity requirement planning (CRP) approach by preemptive earliest due date dispatching (PEDD) 14
3.3. Phases 2 and 3 of MIP approach 16
3.3.1. MIP model 1 for phase 2: minimize total tardiness 16
3.3.2. MIP model 2 for phase 3: minimize total setup times 19
3.4. Heuristic search approaches for phases 2 and 3 20
3.4.1. Search method for discrete decisions in phases 2 and 3 21
3.4.2. Continuous decision for phases 2 and 3 33
3.5. Phase 4: Color-size-level Scheduling 37
4. Computation experiments 41
4.1 Parameters settings 41
4.2. Problem generating procedure 42
4.3 Algorithm parameters design 44
4.4 Results and analysis 45
4.4.1. Comparison of the three meta-heuristic search methods and MIP model in phase 2 45
4.4.2. Comparison of the three meta-heuristic search methods and MIP model in phase 3 45
4.4.3. The results and analysis in phase 4 46
5. Conclusion and future research 54

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