隨著世界人口逐漸增長，作為生活必需品的鞋子，其市場價值也正在逐漸成長中，但由於鞋業屬於勞力密集型產業，並且有許多種不同的產品類型，在生產過程中經常需要符合多樣化但少量生產的目標，常導致產品製造程序改變，換線需求頻繁。本研究針對製鞋廠針車線製程發展基因演算法(Genetic Algorithm, GA)求解工序、人員與機器安排生產線平衡型IV問題，藉由提高同一工作站內工序間的關係，並縮小工作站數，以達到生產線平衡，並妥善分配各工作站所需資源，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，在人員、機台與生產週期時間的限制下，決定最適工作站分配及設計生產線佈置，利用基因演算法求解型IV生產線平衡問題，目標為最大化工作站內之工序間關係，並求得最少工作站數量。本研究蒐集在中國南方某工廠中製造的鞋型之資料用於比較簡單及複雜模型，搭配實驗設計評估演算法及生產系統中之最佳參數設定，及驗證演算法之績效。本研究提出的方法可用來決定在人員及機台限制下，實際可用之生產線佈置且提高針車線製程生產效率，具高度實用價值。

Footwear manufacturing is a traditional labor intensive industry with many kinds of product types and production process. The manual operations in footwear stitching line are hardly replaced by automatic machines and orders from consumer are regularly large variety of products with low-volume. The redesign of lines is required frequently because of the changes of production process.
In this research, a Genetic Algorithm (GA) is proposed to solve Type-IV Assembly Line Balancing Problem (ALBP-IV) which aims to maximize relationship between assigned tasks in one workstation and reach minimal number of workstations simultaneously. According to the real case of limited resources, data are collected from a footwear manufacturing company in South China for comparison of simple and complex models. An approach of design of experiments is applied to evaluate the performance of combinations for genetic parameters and manufacturing system parameters. After experiments, analysis of variance (ANOVA) is implemented for analyzing the results. This research can provide footwear industry for dealing with task assigning problem with constrained labors and machines and is capable of improving production efficiency.

摘要 I
Abstract II
致謝 III
Contents IV
List of Tables VI
List of Figures VII
Chapter1: Introduction 1
1.1 Background 1
1.2 Objectives 3
1.3 Research Method 3
1.4 Organization of Thesis 5
Chapter2: Literature Review 6
2.1 Assembly Line Balancing Problem (ALBP) 6
2.2 Assembly Line Balancing Problem Type IV (ALBP-IV) 9
2.3 Genetic Algorithm 11
Chapter3: Problem Definition 14
3.1 Problem Statement 14
3.2 Notations and Assumptions 19
3.3 Problem Formulation 21
Chapter4: Solution Method 25
4.1 ALBP-IV Solution Module 25
4.2 Genetic Algorithms Approach 26
4.2.1 Encoding 27
4.2.2 Decoding 29
4.2.3 Fitness Evaluation 35
4.2.4 Initial Population 36
4.2.5 Selection 36
4.2.6 Crossover 36
4.2.7 Mutation 37
4.2.8 Termination 38
Chapter5: Computational Study 39
5.1 Illustrated Example 39
5.2 Experimental Design Case 47
5.3 GA Parameter Setting 50
5.3.1 Result of GA parameter setting for simple case 51
5.3.2 Result of GA parameter setting for complex case 56
5.4 System Parameter Setting 60
5.4.1 Result of system parameter setting for simple case 61
5.4.2 Result of system parameter setting for complex case 63
Chapter6: Conclusion 66
Reference 68

Ağpak, K. and H. Gökçen (2007). "A chance-constrained approach to stochastic line balancing problem." European Journal of Operational Research 180(3): 1098-1115.

Agrawal, P. (1985). "The related activity concept in assembly line balancing." International Journal of Production Research 23(2): 403-421.

APICCAPS (2015). "World Footwear Yearbook 2015." World Footwear Yearbook. from https://issuu.com/joanavazteixeira/docs/20150727_snapshot_2015.

Aytug, H., et al. (2003). "Use of genetic algorithms to solve production and operations management problems: a review." International Journal of Production Research 41(17): 3955-4009.

Baybars, I. (1986). "A survey of exact algorithms for the simple assembly line balancing problem." Management science 32(8): 909-932.

Becker, C. and A. Scholl (2006). "A survey on problems and methods in generalized assembly line balancing." European Journal of Operational Research 168(3): 694-715.

Boysen, N., et al. (2007). "A classification of assembly line balancing problems." European Journal of Operational Research 183(2): 674-693.

Carnahan, B. J., et al. (2001). "Incorporating physical demand criteria into assembly line balancing." Iie Transactions 33(10): 875-887.

Dimopoulos, C. and A. M. Zalzala (2000). "Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons." IEEE Transactions on Evolutionary Computation 4(2): 93-113.

Grefenstette, J. J. (1986). "Optimization of control parameters for genetic algorithms." IEEE Transactions on systems, man, and cybernetics 16(1): 122-128.

Gutjahr, A. L. and G. L. Nemhauser (1964). "An algorithm for the line balancing problem." Management science 11(2): 308-315.

Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, U Michigan Press.

Jin, M. and S. D. Wu (2003). "A new heuristic method for mixed model assembly line balancing problem." Computers & Industrial Engineering 44(1): 159-169.

Kabir, M. A. and M. T. Tabucanon (1995). "Batch-model assembly line balancing: A multiattribute decision making approach." International journal of production economics 41(1): 193-201.

Khorasanian, D., et al. (2013). "Two-sided assembly line balancing considering the relationships between tasks." Computers & Industrial Engineering 66(4): 1096-1105.

Kim, Y. K., et al. (1996). "Genetic algorithms for assembly line balancing with various objectives." Computers & Industrial Engineering 30(3): 397-409.

Kucukkoc, I. and D. Z. Zhang (2016). "Mixed-model parallel two-sided assembly line balancing problem: A flexible agent-based ant colony optimization approach." Computers & Industrial Engineering 97: 58-72.

Lee, T. O., et al. (2001). "Two-sided assembly line balancing to maximize work relatedness and slackness." Computers & Industrial Engineering 40(3): 273-292.

Moreira, M. C. O., et al. (2012). "Simple heuristics for the assembly line worker assignment and balancing problem." Journal of heuristics 18(3): 505-524.

Plans, J. and A. Corominas (1999). Modelling and solving the SALB-E problem. Assembly and Task Planning, 1999.(ISATP'99) Proceedings of the 1999 IEEE International Symposium on, IEEE.

Ponnambalam, S., et al. (2000). "A multi-objective genetic algorithm for solving assembly line balancing problem." The International Journal of Advanced Manufacturing Technology 16(5): 341-352.

Rubinovitz, J. and G. Levitin (1995). "Genetic algorithm for assembly line balancing." International journal of production economics 41(1): 343-354.

Scholl, A. and A. Scholl (1999). "Balancing and sequencing of assembly lines."

Sivasankaran, P. and P. Shahabudeen (2014). "Literature review of assembly line balancing problems." The International Journal of Advanced Manufacturing Technology 73(9-12): 1665-1694.

Tasan, S. O. and S. Tunali (2008). "A review of the current applications of genetic algorithms in assembly line balancing." Journal of intelligent manufacturing 19(1): 49-69.

Tsujimura, Y., et al. (1995). "Solving fuzzy assembly-line balancing problem with genetic algorithms." Computers & Industrial Engineering 29(1): 543-547.

Zacharia, P. T. and A. C. Nearchou (2013). "A meta-heuristic algorithm for the fuzzy assembly line balancing type-E problem." Computers & Operations Research 40(12): 3033-3044.