成衣業屬勞力密集性產業，在國際化環境的壓力下，競爭非常激烈。成衣業製程可分為四個主部份，分別為設計/服裝模板、裁剪、車縫與整燙/包裝，其中車縫作業為最關鍵一環，需要最多的人力及物力。在車縫作業中進行生產線平衡規劃，其目的在於妥善指派工序至各工作站，使工作站中之機台能執行所分派之工序，以減少人力需求，提升生產效率，並降低成本。
本論文主要發展成衣業之生產線平衡系統，應用群組基因演算法（Grouping Genetic Algorithm，GGA）開發啟發式演算法。在週期時間已知情況下指派工序到任一工作站，求解最小生產成本。本研究蒐集成衣業生產數據來驗證所發展之生產線平衡系統之績效。成衣車縫作業生產線平衡問題中以成本為考量的研究文獻較少，由實驗結果得知，本研究所發展之生產線平衡系統，是有效求解成本導向之生產線平衡問題的方法，對成衣業的實用價值很高。

Apparel manufacturing is a labor-intensive traditional industry. Under the pressure of internationalization, the competition in this industry is quite drastic. The production process of garments is separated into four main phases: designing/clothing pattern generation, fabric cutting, sewing and ironing/packing. Among these, sewing is the most critical process which needs lots of human and materials resource. The purpose of assembly line balancing problem (ALBP) in sewing lines is to assign tasks to the workstations making the machines of the workstations perform the assigned tasks in order to reduce manpower, increase efficiency and lower the total cost.
This thesis develops a line balancing system for apparel manufacturing industry. Grouping genetic algorithm (GGA) is applied to develop heuristics in the proposed line balancing system. For a given cycle time, tasks are assigned to workstations to minimize the total relevant cost. This research verifies the performance of the proposed line balancing system by collecting data from real apparel manufacturing factory. There is only limited literature in the area of ALBP with cost consideration. The computational results reveal that the line balancing system developed in this thesis is effective in solving cost-oriented assembly line balancing and worker assignment problem (CALBWAP) which has high practical value in apparel industry.

CONTENTS
摘要 i
ABSTRACT ii
致謝 iii
CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Research objective 4
1.3 Research method and procedure 5
1.4 Organization 6
Chapter 2 Literature Review 7
2.1 Assembly Line Balancing Problem 7
2.2 Assembly Line Worker Assignment and Balancing Problem 9
2.3 Cost-oriented Line Balancing Problem 10
2.4 Genetic and Grouping Genetic Algorithm 11
Chapter 3 Problem Definition 13
3.1 Problem Statement 13
3.2 Problem formulation 14
Chapter 4 Methodology 19
4.1 Initial Solution Module 20
4.2 Grouping Genetic Algorithms Approach 24
4.2.1 Encoding 25
4.2.2 Initial population 26
4.2.3 Genetic Operator 28
Chapter 5 Computational Experience 30
5.1 Illustrated examples 30
5.2 Experimental case study 32
5.3 Optimal parameter setting 33
5.4 Result 36
5.4.1 Initial solution of sewing lines example 36
5.4.2 GGA approach of sewing lines example 38
5.5 Analysis experimental design 39
Chapter 6 Conclusion 43
Reference 45

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