鞋子為生活必需品。隨著世界人口成長，鞋業市值非常可觀且預期成長快速。產品設計在鞋業快速成長中增加，製造過程困難度也隨之增加。因為產品製造程序改變，換線需求頻繁。更甚者，針車線由於多種人工操作需求而高度仰賴勞工。如此一來，在勞工及機台限制下，決定最適工作站分配及設計生產線佈置越來越重要。在本研究中，利用基因演算法求解E型生產線平衡問題，目標為最大化生產線效率。且基於工序的多寡，蒐集在中國南方某工廠中製造的兩種鞋型之資料用於比較簡單及複雜模型。利用實驗設計方法評估不同參數組合之績效。並利用變異數分析及Duncan測試來分析並評估成果。本研究提出的方法可用來決定在勞工及機台限制下，實際可用之生產線佈置且同時提高生產線效律及降低生產線閒置時間。

Footwear is the necessaries of life. With increasing population in the world, the footwear market value is considerable and expected to grow rapidly. In a fast growing footwear industry with increasing variety of product designs, the manufacturing process gain its difficulties. The redesign of lines is required frequently because of changes of production process. Moreover, stitching line is highly dependent on operators due to its requirements of numerous manual operations. Thus, with constrained operators and equipments, deciding the optimal assignment to workstations and design the assembly line configuration become more important. In this research, a Genetic Algorithm (GA) is proposed to solve type-E Assembly Line Balancing Problem (ALBP-E) which aims to maximize line efficiency. And based on the number of tasks, manufacturing data of two different specification are collected from a footwear manufacturing company in south China for comparison between simple and complex models. An approach of design of experiments is applied to evaluate performance of different parameters combinations. The output is analyzed and evaluated by using analysis of variance (ANOVA) and Duncan test. The proposed approach can provide applicable configuration in real environment with constrained labors and machines and is capable of increasing the production efficiency and decreasing idle time.

摘要 I
ABSTRACT II
Chapter 1: Introduction 1
1.1 Background 1
1.2 Objectives 3
1.3 Methodology and Procedures 4
1.4 Organization of Thesis 6
Chapter 2: Literature Review 7
2.1 Assembly Line Balancing Problem (ALBP) 7
2.2 Assembly Line Worker Assignment and Balancing Problem (ALWABP) 9
2.3 Type-E Assembly Line Balancing Problem (ALBP-E) 11
Chapter 3: Problem Definition 12
3.1 Problem Statement 12
3.2 Notations and Assumptions 17
3.3 Problem Formulation 19
Chapter 4: Solution Method 22
4.1 Encoding 23
4.2 Decoding 26
4.3 Fitness Evaluation 33
4.4 Initial Population 33
4.5 Selection 33
4.6 Crossover 34
4.7 Mutation 34
4.8 Termination 35
Chapter 5: Computational study 36
5.1 Illustrated Example 36
5.2 Experimental Case Study 44
5.3 Optimal Parameters Setting 44
5.4 Experimental Results and analysis 50
5.4.1 Experimental result of simple case 52
5.4.2 Experimental result of complex case 57
Chapter 6: Conclusions 64
Reference 67

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