製鞋業屬傳統勞力密集性產業，在台灣經濟發展過程中，佔有無可取代之地位。製鞋大致分為鞋面及鞋底兩大製程，而針車作業即為鞋面製程中所需人力最多，最為重要之一環。本研究針對製鞋廠針車線製程發展群組基因演算法(Grouping Genetic Algorithm, GGA)求解資源有限及人員安排生產線平衡型III問題，經由生產線平衡安排，妥善分配各工作站工作負荷，進而減少製程人力需求，提升生產效率。
本研究考量製鞋業生產現場的實際特性，包括多技能員工、技能熟練度、工序時間變異及員工數等，發展GGA求解資源限制情形下之生產線平衡型III問題，工作站總數為已知，指派工序到工作站，使各工作站間工作負荷平衡最大化。本研究亦以製鞋廠實際生產數據搭配實驗設計，尋求演算法及生產系統最佳參數設定，及驗證演算法之績效。此外，本研究亦將預算作為生產線平衡安排限制條件之一，以了解其對目標值之影響。本研究可提供製鞋廠於產線安排上之參考，有效提升其針車線製程效率，具高度實用價值。

Footwear manufacturing is a traditional labor intensive industry, and plays an important role in the economy growth in Taiwan. The manufacturing process of shoes is generally separated into upper process and sole process, and stitching, which requires the most operators, is one of the most critical processes in upper process. This study uses Grouping Genetic Algorithm (GGA) to solve type III Resource-Constrained Assembly Line and Worker Assignment Balancing Problem (RCALWABP) in stitching lines of footwear manufacturing. With balancing of stitching lines, it can let workload of lines more smoothness, reduce operator requirement and increase production efficiency.
This study takes into accounts several practical characteristics in footwear manufacturing, including multi-skilled operators, operator efficiency, task time variance and number of operators. Type III RCALWABP in stitching lines is solved using GGA to maximize workload smoothness and allocate the workload evenly with a given number of workstations. Real data from footwear factories is collected and used to evaluate the performance of GGA based on experimental design. The optimal parameters setting of GGA and production system are also obtained. Furthermore, this study takes budget as one the constraint, and uses it to verify the effect to objective value. This study can provide some idea for footwear factory to assign tasks to workstations and the efficiency can be improved.

CONTENTS
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 3
1.3 Research Method 4
1.4 Organization of Thesis 5
Chapter 2: Literature Review 7
2.1 Footwear Manufacturing 7
2.2 Assembly Line Balancing Problem (ALBP) 8
2.3 Assembly Line Worker Assignment and Balancing Problem (ALWABP) 11
2.4 Grouping Genetic Algorithm 13
Chapter 3: Problem Definition 15
3.1 Problem Statement 15
3.2 Notations and Assumptions 19
3.3 Problem Formulation 20
Chapter 4: Solution Method 24
4.1 RCALWABP-III Solution Module 24
4.2 Grouping Genetic Algorithms Approach 25
4.2.1 Encoding 27
4.2.2 Initial population 28
4.2.3 Fitness evaluation 29
4.2.4 Selection 30
4.2.5 Crossover 31
4.2.6 Mutation 33
4.2.7 Replacement of new population 33
4.2.8 Termination 34
Chapter 5: Computational Study 35
5.1 Illustrated Example 35
5.2 Experimental Design Case 42
5.3 GGA Parameter Setting 43
5.3.1 Result of GGA parameter setting for simple case 46
5.3.2 Result of GGA parameter setting for complex case 48
5.4 System Parameter Setting 50
5.4.1 Result of system parameter setting for simple case 52
5.4.2 Result of system parameter setting for complex case 63
5.5 Model Extension 73
Chapter 6：Conclusion 81
Reference 83

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