成衣業造就台灣經濟成長，屬勞力密集性產業，車縫作業為其製程最重要一環，所需人力最多。透過生產線平衡安排，能減少人力需求，提升生產效率，降低人工成本並縮短生產週期時間。本論文將針對成衣廠車縫製程發展群組基因演算法(Grouping Genetic Algorithm, GGA)分別求解生產線平衡型I及型II問題。
本論文第一階段將發展GGA求解生產線平衡型I問題，在週期時間已知情況下指派工序到任一工作站，並期望使用最少工作站數。第二階段則以GGA求解生產線平衡型II問題，在工作站總數已知下，求解最小週期時間以尋找生產線之最大產出率。由於工作站總數已知是求解此問題之必要條件，故可視為第一階段之延伸探討。成衣車縫作業生產線平衡型I、型II問題研究文獻較少，但對成衣業的實用價值很高。本研究將考慮成衣生產的實際特性，包括多技能員工，技能熟練度，學習曲線等。將以實際成衣廠生產數據進行演算法參數設定，並搭配實驗設計分析驗證所發展演算法之績效。

Apparel manufacturing is a operator-intensive traditional industry helping the economy growth in Taiwan. The most critical manufacturing process is sewing, as it generally involves a great number of manual operations. A balanced sewing line can reduce operator requirement, increase production efficiency, decrease operator cost, and reduce production cycle time. This paper uses Grouping Genetic Algorithm (GGA) to solve types I and II Resource-Constrained Assembly Line and Worker Assignment Balancing Problem (RCALWABP) in sewing lines of apparel industry.

Type I RCALWABP in sewing lines was solved using GGA to minimize the number of workstations for a given cycle time. Type II RCALWABP was solved using GGA to minimize the cycle time and maximize the throughput for a given number of workstations. Type II RCALWABP is generally considered as the extension of Type I RCALWABP. The solution of types I and II RCALWABP in sewing lines has high practical value, but there is only limited literature in this area.

This paper takes into account several practical characteristics in apparel industry, including multi-skill operators, operator efficiency, and learning curve. Real data from apparel factories will be used to set the best parameters of GGA and evaluate GGA’s performance based on experimental design.

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 4
1.3 Methodology and procedures 5
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) 11
2.3 Genetic and Grouping Genetic Algorithm 12
Chapter 3: Problem definition 14
3.1 Problem Statement 14
3.2 Notations and Assumptions 18
3.3 Problem formulation 20
3.3.1 RCALWABP-I 20
3.3.2 RCALWABP-II 23
Chapter 4：Solution Method 25
4.1 Initial Solution Module 26
4.1.1 Modification Largest-Candidate-Rule Heuristics for RCALWABP-I 26
4.1.2 Modification Largest-Candidate-Rule Heuristics for RCALWABP-II 30
4.2 Grouping Genetic Algorithms Approach 35
4.2.1 Encoding 35
4.2.2 Initial population 37
4.2.3 Fitness evaluation 38
4.2.4 Selection 38
4.2.5 Crossover 39
4.2.6 Mutation 40
4.2.7 Termination 40
Chapter 5：Computational study 42
5.1 illustrated example 42
5.1.1 Initial solution result of RCALWABP-I 45
5.1.2 Initial solution result of RCALWABP-II 48
5.2. Experimental case study 51
5.3. Optimal parameter setting 52
5.3.1 Optimal parameter setting result of RCALWABP-I 52
5.3.2 Optimal parameter setting result of RCALWABP-II 55
5.4. Analysis experimental design 56
5.4.1 Analysis experimental design result of RCALWABP-I 57
5.4.2 Analysis experimental design result of RCALWABP-II 60
Chapter 6：Conclusions 62
Reference 64
Appendix 69

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

Blum, C., & Miralles C. (2011). On solving the assembly line worker assignment and balancing problem via beam search, Computers and Operations Research, 38(1), 328-339.

Borba, L., & Ritt, M. (2014). A heuristic and a branch-and-bound algorithm for the Assembly Line Worker Assignment and Balancing Problem, Computers & Operations Research, 45, 87-96.

Boysen, N., Fliedner, M., & Scholl, A. (2007). A classification of assembly line balancing problems, European Journal of Operational Research, 183, 674–693.

Boysen, N., Fliedner, M., & Scholl, A. (2008). Assembly line balancing: Which model to use when?, International Journal of Production Economics, 111, 509–528.

Brown, E.C., & Sumichrast, R.T. (2005). Evaluating performance advantages of grouping genetic algorithms, Engineering Applications of Artificia l Intelligence, 18, 1-12.

Bukchin, J., Dar-El, E. M., & Rubinovitz, J. (2002). Mixed model assembly line design in a make-to-order environment, Computers and Industrial Engineering, 41, 405-421.

Bukchin, Y., & Sofer, T. (2011). Bi-directional work sharing in assembly lines with strict and flexible assembly sequences, International Journal of Production Research, 49, 14, 4377-4395.

Capacho, L., & Pastor, R. (2008). ASALBP: the alternative sub graphs assembly line balancing problem, International Journal of Production Research, 46, 13, 3503-3516.

Chaves A.A., Lorena L.A.N., & Miralles C. (2009). Hybrid metaheuristic for the assembly line worker assignment and balancing problem, Lecture Notes in Computer Science, 5818, 1–14.

Chaves A.A., Miralles C.,& Lorena L.A.N. (2007). Clustering search approach for the assembly line worker assignment and balancing problem. In: Proceedings of the 37th international conference on computers and industrial engineering. Alexandria, Egypt, 1469–78.

Chen, R.S., Lu, K.Y., & Yu, S.C. (2002). A hybrid genetic algorithm approach on multi-objective of assembly planning problem, Engineering Applications of Artificial Intelligence, 15, 447-457.

Corominas A., Pastor R., & Plans J. (2008). Balancing assembly line with skilled and unskilled workers, Omega, 36, 1126–32.

Emde, S., Boysen, N., & Scholl, A. (2010). Balancing mixed-model assembly lines: a computational evaluation of objectives to smoothen workload, International Journal of Production Research, 48, 11, 3173-3191.

Falkenauer, E. (1992). The grouping genetic algorithm-widening the scope of the GAs, Proceedings of the Belgian Journal of Operations Research, Statistics and Computer Science, 33, 79-102.

Falkenauer, E. (1996). A hybrid grouping genetic algorithm for bin packing, Journal of Heuristics, 2, 5-30.

Gamberini, R., Gebennini, E., Grassi, A., & Regattieri, A. (2009). A multiple single-pass heuristic algorithm solving the stochastic assembly line rebalancing problem, International Journal of Production Research, 47, 8, 2141-2164.

Grangeon, N., Leclaire, P., & Norre, S. (2011). Heuristics for the re-balancing of a vehicle assembly line, International Journal of Production Research, 49, 22, 6609-6628.

Haq, A.N., Jayaprakash, J. & Rengarajan, K. (2006). A hybrid genetic algorithm approach to mixed-model assembly line balancing, The International Journal of Advanced Manufacturing Technology, 28, 337-341.

Heimerl, C., & Kolisch, R. (2009). Work assignment to and qualification of multi-skilled human resources under knowledge depreciation and company skill level targets, International Journal of Production Research, 48, 13, 3759-3781.

Hwang, R.K., & Katayama, H. (2009). A multi-decision genetic approach for workload balancing of mixed-model U-shaped assembly line systems, International Journal of Production Research, 47, 14, 3797-3822.

Kara, Y., Gokcen, H., & Atasagun, Y. (2010). Balancing parallel assembly lines with precise and fuzzy goals, International Journal of Production Research, 48, 6, 1685-1703.

Kara, Y., & Tekin, M. (2009). A mixed integer linear programming formulation for optimal balancing of mixed-model U-lines, International Journal of Production Research, 47, 15, 4201-4233.

Karabati, S., & Sayin, S. (2003). Assembly line balancing in a mixed-model sequencing environment with synchronous transfers, European Journal of Operational Research, 149, 417-429.

Kim, Y., Kim, Y., & Kim, Y. (1996). Genetic algorithms for assembly line balancing with various objectives, Computers and Industrial Engineering, 30, 397–409.

Kim, Y.K., Song, W.S., & Kim, J.H. (2009). A mathematical model and a genetic algorithm for two-sided assembly line balancing, Computers and Operations Research, 36, 853-865.

Lit, P.D., Falkenaurer, E., & Delchambre, A. (2000). Grouping genetic algorithms: an efficient method to solve the cell formation problem, Mathematics and Computers in Simulation, 51, 257-271.

Mattfeld, D. C. & Bierwirth, C. (2004). An efficient genetic algorithm for job shop scheduling with tardiness objectives, European Journal of Operational Research, 150, 616-630.

Miralles, C., Garcia-Sabater, J.P., Andrés, C., & Cardos, M. (2007). Advantages of assembly lines in sheltered work centers for disabled：A case study, International Journal of Production Economics, 110, 187–197.

Miralles C., Garcia-Sabater J.P., Andres C., & Cardos M. (2008). Branch and bound procedures for solving the assembly line worker assignment and balancing problem: application to sheltered work centers for disabled, Discrete Applied Mathematics, 156, 352–67.

Nearchou, A.C. (2008). Multi-objective balancing of assembly lines by population heuristics, International Journal of Production Research, 46, 8, 2275-2297.

Ozcan, U., Cercioglu, H., Gokcen, H., &Toklu, B. (2010a). Balancing and sequencing of parallel mixed-model assembly lines, International Journal of Production Research, 48, 17, 5089-5113.

Ozcan, U., Gokcen, H., & Toklu, B. (2010b). Balancing parallel two-sided assembly lines, International Journal of Production Research, 48, 16, 4767-4784.

Ozcan, U., & Toklu, B. (2010). Balancing two-sided assembly lines with sequence-dependent setup times, International Journal of Production Research, 48, 18, 5363-5383.

Pankratz, G. (2005). A grouping genetic algorithm for the pickup and delivery problem with time window, OR Spectrum, 27, 21–41.

Pastor, R., & Ferrer, L. (2009). An improved mathematical program to solve the simple assembly line balancing problem, International Journal of Production Research, 47, 11, 2943-2959.

Pastor, R., & Martino, L. (2010). Heuristic procedures for solving the general assembly line balancing problem with setups, International Journal of Production Research, 48, 6, 1787-1804.

Schaffer, J. D., Richard, A., Caruana, L., Eshelman, J. & Rajarshi, D. (1989), A Study of Control Parameters Affecting Online Performance of Genetic Algorithm for Function Optimization, The 3rd International Conference on Genetic Algorithms and Their Applications, 51-60.