半導體產業面對全球化的市場競爭，為了達到滿足顧客需求並同時降低企業生產總成本之目的，追求更高的「產品達交狀況」便成了企業能否增加其生產利潤的關鍵因素。半導體封裝屬於彈性流程式生產系統，考量其接單式生產的特性與較前段晶圓製造具較短的生產週期時間，如何透過良好的生產管制來滿足產品交期向來都是一個挑戰。
本研究定義了半導體封裝中的機台配置問題，考量生產系統的限制條件，探討平行機台的最適分配以追求訂單具有高達交率，同時降低因生產延誤而產生的額外費用。為了仿效生產系統的隨機因素與現場實務上諸多的細節，可靠的商品達交情形估計需要仰賴電腦模擬。然而在受限時間之下，電腦模擬耗時甚久的缺點，往往不利於直接結合最佳化方法求解。因此本研究採用的模擬最佳化方法整合了實驗設計與後設模型(Metamodel)，可有效地降低執行電腦模擬所需的總運算時間，讓受限時間下的決策需求變得可行。本研究所提出的方法將透過求解範例問題來評估其可行性，而實驗結果顯示此方法能充分運用有限的運算時間提供可行的機台分配方案，並逐步降低生產延誤成本。

Parallel machine allocation (PMA) is a commonly encountered problem in manufacturing. The purpose of PMA is to determine the appropriate number of machines for a particular production stage so as to well utilize the available production resources, and to achieve a high throughput rate. However, the performance of machine allocation is difficult to measure in a complex production system such as semiconductor manufacturing. Discrete event simulation (DES) is an effective tool to capture the behavior of the complex system because it allows to take into consideration all the important details in the manufacturing process. Unfortunately, DES often takes more computing time to obtain reliable estimates. In this study, we propose a simulation optimization approach to address a parallel machine allocation problem in a flow shop production system. This approach is based on the metamodeling technique that can achieve a great deal of savings on the computing budget. An empirical problem based on real data was conducted to demonstrate the feasibility of the proposed method in practical setting.

致謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 vii
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 論文架構 3
第二章 文獻探討 5
2.1 延遲交貨成本 5
2.2 派工方法 5
2.3 半導體封裝之相關研究 6
2.4 彈性式生產流程(Flexible Flow Shop，FFS) 6
2.5 迴流(Re-entrant) 7
2.6 隨機最佳化(Stochastic Optimization) 7
2.7 後設模型(Metamodel) 8
第三章 問題定義 11
3.1 半導體封裝 11
3.2 數學模型 13
第四章 求解方法 16
第五章 數值實驗 24
5.1 問題定義 24
5.2 數值結果 25
第六章 實驗計畫與結果分析 27
6.1 實驗計畫 27
6.2 結果分析 32
第七章 結論與未來展望 33
文獻/參考書目 35
附錄一 40

Banks, J. (1998). Handbook of simulation: principles, methodology, advances, applications, and practice. John Wiley & Sons.
Bard, J. F., Gao, Z., Chacon, R., & Stuber, J. (2013). Daily scheduling of multi-pass lots at assembly and test facilities. International Journal of Production Research, 51(23-24), pp. 7047-7070.
Barton, R. R., & Martin, M. (2006). Metamodel-based simulation optimization. Handbooks in operations research and management science. Handbooks in operations research and management science, 13, pp. 535-574.
Boxma, O. J., Kan, A. R., & van Vliet, M. (1990). Machine allocation problems in manufacturing networks. European Journal of Operational Research, 45(1), pp. 47-54.
Chang, H. K., Huang, H. Y., & Yang, P. S. (2014). Vehicle fleet sizing for automated material handling systems to minimize cost subject to time constraints. IIE Transactions, 46(3), pp. 301-312.
Chang, K. H., & Hsieh, L. Y. (2016). Determination of Wafer Start Mix in Semiconductor Manufacturing During New Technology Ramp-Up: Model, Solution Method, and an Empirical Study, Semiconductor Manufacturing. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(2), pp. 294-302.
ChangKuo-Hao, HongL.Jeff, & WanHong. (2013). Stochastic trust-region response-surface method (strong)-a new response-surface framework for simulation optimization. INFORMS Journal on Computing, 2, 頁 230-243.
Chen, J. C.; Chen, K. H; Wu, J. J.; Chen, C. W.;. (2008). A study of the flexible job shop scheduling problem with parallel machines and reentrant process. The International Journal of Advanced Manufacturing Technology, 39(3-4), pp. 344-354.
Chen, L. Z. (2004). Simultaneous Job Scheduling and Resource Allocation on Parallel Machines. Annals of Operations Research, 129(1-4), pp. 135-153.
Choi, H. S., Kim, J. S., & Lee, D. H. (2011). Real-time scheduling for reentrant hybrid flow shops: A decision tree based mechanism and its application to a TFT-LCD line. Expert systems with Applications, 38(4), pp. 3514-3521.
Dabbas, R. M., & Fowler, J. W. (2003). A new scheduling approach using combined dispatching criteria in wafer fabs. IEEE Transactions on Semiconductor Manufacturing, 16(3), pp. 501-510.
Deng, Y., Bard, J. F., Chacon, G. R., & Stuber, J. (2010). Scheduling back-end operations in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 23(2), pp. 210-220.
Fu, M., Askin, R., Fowler, J., Haghnevis, M., Keng, N., Pettinato, J. S., & Zhang, M. (2011). Batch production scheduling for semiconductor back-end operations. Semiconductor Manufacturing. IEEE Transactions on Semiconductor Manufacturing, 24(2), pp. 249-260.
Graves, S. C., Meal, H. C., Stefek, D., & Zeghmi, A. H. (1983). Scheduling of re-entrant flow shops. Journal of Operations Management(3), pp. 197-207.
Guo, R. S., Chiang, D., & Pai, F. Y. (2007). A WIP-based exception-management model for integrated circuit back-end production processes. The International Journal of Advanced Manufacturing Technology, 33(11-12), pp. 1263-1274.
Holland, J. H. (1973). Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 2(2), pp. 88-105.
Hsieh, L. Y., Chang, K. H., & Chien, C. F. (2014). Efficient development of cycle time response surfaces using progressive simulation metamodeling. International Journal of Production Research, 52(10), pp. 3097-3109.
Huang, C. J., Chang, K. H., & Lin., J. T. (2012). Optimal vehicle allocation for an automated materials handling system using simulation optimisation. International Journal of Production Research, 50(20), pp. 5734-5746.
Jungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2009). A comparison of scheduling algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria. Computers & Operations Research, 36(2), pp. 358-378.
Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2), pp. 83-97.
Montgomery, D. C. (2008). Design and analysis of experiments. John Wiley & Sons.
Mosheiov, G. (2001). Parallel machine scheduling with a learning effect. Journal of the Operational Research Society, 52(10), pp. 1165-1169.
Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons.
Panwalkar, S. S., & Iskander., W. (1977). A survey of scheduling rules. Operations research, 25(1), pp. 45-61.
Sethuraman, J., & Squillante, M. S. (1999, January). Optimal scheduling of multiclass parallel machines. SODA, pp. 963-964.
Sivakumar, A. I., & Chong, C. S. (2001). A simulation based analysis of cycle time distribution, and throughput in semiconductor backend manufacturing. Computers in Industry, 45(1), pp. 59-78.
Song, W. T. (1996). On the estimation of optimal batch sizes in the analysis of simulation output. European Journal of Operational Research, 88(2), pp. 304-319.
Van Zant, P., & Chapman. (2000). Microchip fabrication: a practical guide to semiconductor processing (Vol. 5). New York: McGraw-Hill.
Wolsey, L. A. (1998). Integer programming. New York: Wiley.
Yang, T. (2009). An evolutionary simulation–optimization approach in solving parallel-machine scheduling problems–A case study. Computers & Industrial Engineering, 56(3), pp. 1126-1136.
資策會. (2015). 2015上半年臺灣消費者行動裝置使用與媒體接觸行為分析報告. 資策會 創新應用服務研究所.