研究針對半導體封裝廠之特殊混合流程型生產環境進行訂單指派規劃，其規劃主要分為兩個部分：一為將訂單指派至適當之生產產線；二為將訂單指派至各站適當之加工機群。主要探討該產業三個瓶頸製程，上片、銲線以及模壓，加工過程中有一載具轉換機制而造就其拆批與集批特性；且各站皆有許多不同型號/等級的機型，每個機型有許多機台，因此各站皆有非等效平行機群及完全相同的平行機台之特性。各產品依照其高低階程度或規格的不同，在各站並非所有的機群都能進行加工。由於該產業特殊的生產特性，其指派問題相當困難且複雜，若是指派不當，容易造成後續許多問題。舉例來說，若是指派到的產線沒有可以生產該訂單的機型，便須換線生產或是移動機台，換線生產容易有混料或混批的情況，而移動機台造成移動成本以及延伸後續其它問題。
　　本研究運用模擬最佳化的手法來解決三產線、三站別、多機型、多產品以及多訂單的指派規劃，考量其拆批、集批、非等效平行機台與完全相同平行機台共存之特性，並考慮各訂單允入產線與允入機群之限制，以最小化平均一張訂單之流程時間為目標，找到適合且較好之各訂單生產產線與加工機群指派方案。在面臨求解方案數過多時，本研究利用粒子群最佳化演算法進行解空間之搜尋，並加入鄰域搜尋法改善該演算法在面臨求解空間過大時可能會陷入區域最佳解的情況，同時利用OCBA有效地分配模擬資源及節省模擬時間，最後，本研究比較不同的方法，證明粒子群最佳化演算法結合OCBA及鄰域搜尋方法，能更快的搜尋到最佳解及有更好的效率，也提供實務上一種方法的參考依據。

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍 3
1.4 研究步驟與方法 4
第二章 文獻回顧 7
2.1 半導體封裝產業產能規劃之層級 7
2.2 混合流程型生產（Hybrid Flow Shop）之文獻 10
2.2.1 啟發式方法 12
2.2.2 多粒子隨機搜尋 13
2.2.3 混合式搜尋演算法 14
2.3 模擬最佳化 15
2.3.1 OCBA（Optimal Computing Budget Allocation） 16
2.3.2 粒子群最佳化搜尋法 20
第三章 半導體封裝產業概述與問題定義 23
3.1 半導體封裝產業概述 24
3.2 半導體封裝產業特性 26
3.3 半導體封裝廠生產計劃介紹 30
3.4 訂單分配產線與加工機群指派流程現況與分析 33
3.5 現行作法所面臨的問題 36
3.6 改善方向 39
3.7 問題描述 40
3.7.1 半導體封裝製程之載具轉換流程 40
第四章 問題定義與方法論 46
4.1 問題定義與假設 46
4.1.1 訂單生產產線與機群指派問題模擬模式 48
4.2 方法論 50
4.2.1 粒子群最佳化演算法 51
4.2.2 OCBA 59
4.2.3 鄰域搜尋法 61
4.2.4 PSOOL 64
4.3 範例情境 65
4.3.1 範例情境說明 65
4.3.2 模擬模式建構 68
4.3.3 範例實驗結果與績效分析 76
第五章 實例驗證與結果分析 80
5.1 實例情境 80
5.2 模擬模式建構 85
5.2.1 模擬模式建構目的 85
5.2.2 模擬範圍與細緻度 86
5.2.3 模擬模式 87
5.3 實例參數分析 92
5.3.1 實驗目的 92
5.3.2 粒子群最佳化演算法（PSO）參數設定 93
5.3.3 OCBA之參數設定 96
5.3.4 鄰域搜尋法（Local Search）之參數設定 99
5.3.5 小結 100
5.4 方法比較 101
5.4.1 PSO、PSOOCBA、PSOL、PSOOL方法比較 102
5.4.2 PSOOCBA與GAOCBA方法比較 117
5.4.3 PSOOL與現行方法比較 118
第六章 結論與建議 123
6.1 結論 123
6.2 建議 124
參考文獻 126

1. 林則孟，「系統模擬-理論與應用」，滄海書局，2001
2. 詹詩敏，”半導體封裝廠之機台配置問題”，國立清華大學工業工程與工程管理學系，碩士論文，2011
3. 黃思孟，”半導體封裝廠之短期訂單與機台指派問題”，國立清華大學工業工程與工程管理學系，碩士論文，2012
4. 楊敏雄，”整合變動鄰域搜尋法和粒子群最佳化演算法於平行機台加工之研究-以晶矽太陽能產業為例”，私立東海大學工業工程與經營資訊學系，碩士論文，2012
5. 鄭書豪，”CONWIP生產管制架構於IC封裝產業之應用”，國立清華大學工業工程與工程管理學系，碩士論文，1998
6. 徐梅芳，”半導體封裝廠產能規劃研究”，私立中原大學工業工程學系，碩士論文，2005
7. Akrami, B., B. Karimia and S. M. Moattar Hosseini, “Two metaheuristic methods for the common cycle economic lot sizing and scheduling in flexible flow shops with limited intermediate buffers: The finite horizon case”, Applied Mathematics and Computation, 183(1), 634-645 (2006)
8. Chen, C. H., J. Lin, E. Yucesan and S. E. Chick, ”Simulation Budget Allocation for Further Enhancing the Efficiency of Ordinal Optimization”, Discrete Event Dynamic Systems, 10, 251–270 (2000)
9. Choong, F., S. Phon-Amnuaisuk, M. Y. Alias, “Metaheuristic methods in hybrid flow shop scheduling problem”, Expert Systems with Applications, 38, 10787–10793 (2011)
10. Carson, J. S., “AutoStat Output Statistical Analysis for AutoMod Users”, Proceedings of the 1996 Winter Simulation Conference, 492-499 (1996)
11. Garey, M. R. and D. S. Johnson, ”Computers and intractability: a guide to the theory of NP-completeness”, San Francisco, Freeman (1979)
12. He, D., L. H. Lee, C. H. Chen, M. Fu and S. Wasserkrug, “Simulation Optimization Using the Cross-Entropy Method with Optimal Computing Budget Allocation”, ACM Transactions on Modeling and Computer Simulation, 20 (1), 133–161 ( 2010)
13. Henderson, S. G. and B. L. Nelson, “Handbook in OR & MS”, Vol. 13Copyright (2006)
14. Jenabi, M., S. M. T. Fatemi Ghomi, S. A. Torabi and B. Karimi, “Two hybrid meta-heuristics for the finite horizon ELSP in flexible flow lines with unrelated parallel machines”, Applied Mathematics and Computation, 186(1), 230-245 (2007)
15. Jin, Z. H., K. Ohno, T. Ito and S. E. Elmaghraby, “Scheduling hybrid flow shops in printed circuit board assembly lines”, Production and Operations Management, 11(2), 216-230 (2002)
16. Kahraman, C., O. Engin, İ. Kaya and R. E. Öztürk, “Multiprocessor task scheduling in multistage hybrid flow-shops: a parallel greedy algorithm approach”, Applied Soft Computing, 10, 1293–1300 (2010)
17. Kennedy, J. and R. Eberhart, “Particle Swarm Optimization”, Proc. IEEE International Conference on Neural Networks, Australia, 1942–1948 (1995)
18. Lee, L. H., E. P. Chew, S. Y. Teng and Y. Chen, “Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem”, European Journal of Operational Research, 189, 476-491 (2008)
19. Engin, E., G. Ceran and M. K. Yilmaz, “An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems”, Applied Soft Computing, 11, 3056–3065 (2011)
20. Oğuz, C. and M. Ercan, “A genetic algorithm for hybrid flow-shop scheduling with multiprocessor tasks”, Journal of Scheduling, 8(4), 323–351 (2005)
21. Oguz, C., Y. Zinder, V. H. Do, A. Janiak and M. Lichtenstein, “Hybrid flow-shop scheduling problems with multiprocessor task systems”, European Journal of Operational Research, 152(1), 115-131 (2004)
22. Ruiz, R. and J. A. Vazquez-Rodriguez, ” The hybrid flow shop scheduling problem”, European Journal of Operational Research, 205(1), 1-18 (2010)
23. Ribas, I., R. Leisten and J. M. Framin, ”Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective”, Computers & Operations Research, 37, 1439–1454 (2010)
24. Sawik, T.,”Mixed Integer Programming for Scheduling Flexible Flow Lines with Limited Intermediate Buffers”, Mathematical and Computer Modelling, 31, 39-52 (2000)
25. Bertel, S. and J. C. Billaut, “A genetic algorithm for an industrial multiprocessor flow shop scheduling problem with recirculation”, European Journal of Operational Research, 159 (3), 651–662 (2004)
26. Shiau, D. F., S. C. Cheng and Y. M. Huang, “Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm”, Expert Systems with Applications, 34(2), 1133-1143 (2008).
27. Tseng, C. T., C. J. Liao and K. L. Huang,”Minimizing total tardiness on a single machine with controllable”, Computers and Operations Research, 36(6), 1852–1858 (2009)
28. Tseng, C. T., C. J. Liao and T. X. Liao, ”A note on two-stage hybrid flowshop scheduling with missing operations”, Computers and Industrial Engineering, 54(3), 695-704 (2008)
29. Tseng, C. T. and C. J. Liao, ”A particle swarm optimization algorithm for hybrid flowshop scheduling with multiprocessor tasks”, International Journal of Production Research, 46, 4655-4670 (2008)
30. Vollman, T., W. Berry, D. Whybark and F. Jacobs, ”Manufacturing Planning and Control for Supply Chain Management”, McGraw-Hill, 5th international edition (2004)
31. Valerie, B. G., “Hybrid flow shop scheduling with precedence constraints and time lags to minimize maximum lateness”, International Journal of Production Economics, 64(1-3), 101-111 (2000)
32. Ying, K. C., “Iterated greedy heuristic for multiprocessor task scheduling problems”, Journal of the Operations Research Society, 1-8 (2008)
33. Ying, K. C. and S. W. Lin, “Multiprocessor task scheduling in multistage hybrid flowshops: an ant colony system approach”, International Journal of Production Research, 44, 3161-3177 (2006).
34. Yang, T., Y. Kuo and C. Cho, “A genetic algorithms simulation approach for the multi-attribute combinatorial dispatching decision problem”, European Journal of Operational Research, 176(3), 1859-1873 (2007)
35. Chen, Y. Y., C. Y. Cheng , L. C. Wang , T. L. Chen, “A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry”, Int. Journal Production Economics,141, 66–78 (2013)