本研究所探討之原料藥生產為從事人或動物用醫藥品原料製造之行業，在製藥產業中扮演相當重要的原料供應角色。而在美國食品藥物管理局（Food and Drug Administration, FDA）的規範下，若在無法確保藥品狀態與品質的情況下，不可貿然進行生產，顯見品質檢驗對於原料藥產業的高重要性。
品質檢驗部門的任務是提供工廠與倉庫檢驗的服務，但由於品質檢驗流程前置時間長、變異大且可控度差等特性，導致檢驗報告通常具有高比例的延誤，而其中又以在製程中檢驗（In-process Inspection，簡稱IPI）對於工廠生產進度具有直接性的影響。一旦檢驗時間有所延誤，除了造成工廠生產停滯外，可能導致在製程中的藥品反應過度而破壞藥品品質，整批必須重工或報廢。因此，如何縮短工廠等待檢驗報告完成的時間為本研究主要探討之議題。
本研究針對案例公司的品質檢驗部門，依現況問題找出改善契機，在考量產業特性與限制下進行流程設計，提出多個等候設計與派工邏輯之方案，藉由離散事件模擬的輔助建立方案情境，再利用田口方法（Taguchi Method）結合多準則決策方法（Multiple Criteria Decision-making Method，MCDM）來求解多重品質特性之最佳化問題，分析並評估出最適用的檢驗流程控制方案，並以此作為決策者在設計與改善系統時的參考。最後以S/N比（Signal to Noise Ratio）與敏感度分析來衡量系統的穩健性，並利用單反應實驗來作比較。
經由結果與比較分析可觀察出，本研究所提出之模型在服務水準、週期時間以及最大延遲時間上皆優於現況系統之績效，約有18%~28%的改善幅度，顯示本研究模型確實可有效減少工廠等候時間，且增強IPI的反應能力，並降低藥品變質的機率。同時根據單反應最佳化的評比結果可觀察出，本研究所探討田口方法結合TOPSIS方法，在追求本研究之改善目標時，雖然在部分指標未達最佳，但仍屬於可接受範圍內，且確實能得到一整體最佳解。

In the pharmaceutical industry, quality inspection and management are very significant in the manufacturing process. Due to the long lead-times, high variability and low controllability in the quality inspection procedure, the problem of delayed inspection reports are investigated with the manufacturing team in the factory.
This research based on the current problem has developed the guidelines for improvement. According to the industry features and restrictions that carry out process design, it proposes many different queuing and dispatching scenarios by using discrete-event simulations that generate different values for performance indexes. This multiple quality characteristic problem can be modeled by combining the Taguchi method with the multiple criteria decision-making method (MCDM). This is more effective than the traditional Taguchi method that only focus on a single quality characteristic to search for the best combination of parameters.
This research accomplished identifying the best scenario that is superior to the current situation for the quality inspection procedure, which includes the service level, the cycle time, and the max delayed time which is about eighteen to twenty eight percent of an improvement in proportion. The results revealed the best scenario could reduce production waiting time effectively and increase response capability.
A practical example is used to construct the simulation model for comparing different queuing scenarios. Considering the industry features and restrictions, it proposes appropriate queuing heuristic dispatching methods for a feasible case study and it has been implemented in the monitoring of manufacturing processes.

許志義，2003，多目標決策，五南圖書，台北市。
經濟部，2014，生技產業白皮書，經濟部工業局。
簡禎富，2014，決策分析與管理：紫式決策分析以全面提升決策品質，二版，雙葉書廊。
Banks, J., Carson, J.S., Nelson, B.L., and Nicol, D.M., 2010. Discrete-Event System Simulation, 5th ed, Prentice Hall, Upper Saddle River.
Berry, L. L., and Parasuraman, A., 2004. Marketing services: Competing Through Quality, Simon and Schuster, New York.
Barron, F. H., and Barrett, B. E., 1996. The Efficacy of Smarter : Simple Multi-Attribute Rating Technique Extended to Ranking, Acta Psychologica, 93(1-3), 23-26.
Cerdeira, J. O., Figueiredo, R. M., Pereira, A., and Requejo, C., 2011. Scheduling with sequence-dependent batch setup times: planning tests for a pharmaceutic industry, International Journal of Mathematical Modelling and Numerical Optimisation, 2(3), 273-287.
Eberle, L. G., Sugiyama, H., and Schmidt, R., 2014. Improving lead time of pharmaceutical production processes using Monte Carlo simulation, Computers & Chemical Engineering, 68, 255-263.
Fitzsimmons, J. A., and Fitzsimmons, M. J., 2008. Service Management: Operations, Strategy, and Information Technology, 6th Edition, McGraw Hill, United State.
Hopp, W. J. and Spearman, M. L., 2008. Factory Physics. Waveland Press, United States.
Hall, R. W., 1991. Queueing Methods for services and manufacturing, Prentice-Hall, United States.
Hwang, C. L., and Yoon, K., 1981. Multiple Attribute Decision Making : Methods and Applications, Springer-Verlag, New York.
IMS Health, 2013. IMS Health Market Prognosis, United States.
ISO 10360–7 , 2011. Geometrical Product Specifications (GPS) – Acceptance and reverification tests for Coordinate Measuring Machines (CMM), International Organization for Standardization.
Jouini, O., Dallery, Y., Akşin, Z., 2009. Queueing models for full-flexible multi-class call centers with real-time anticipated delays, International Journal of Production Economics, 120(2), 389-399.
Kelton, W. D., Sadowski, R. P., Swets, N. B., 2010. Simulation with Arena, 5th Edition, McGraw Hill, New York.
Kuo, Y., Yang, T., Cho, C., and Tseng, Y. C., 2008. Using simulation and multi-criteria methods to provide robust solutions to dispatching problems in a flow shop with multiple processors, Mathematics and Computers in Simulation, 78(1), 40-56.
Kim, G., Park, C. S., and Yoon, K. P., 1997. Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement. International Journal of Production Economics, 50, 23-33.
Lamothe, J., Marmier, F., Dupuy, M., Gaborit, P., and Dupont, L., 2012. Scheduling rules to minimize total tardiness in a parallel machine problem with setup and calendar constraints, Computers & Operations Research, 39(6), 1236-1244.
Laperrire, L., and Reinhart, G., 2014. CIRP Encyclopedia of Production Engineering, Springer Publishing Company, Incorporated, Germany.
Martinich, J. S., 2008. Production and operations management: An applied modern approach, John Wiley & Sons, United States.
Omar, M. K., and Teo, S., 2007. Hierarchical production planning and scheduling in a multi-product, batch process environment, International Journal of Production Research, 45(5), 1029-1047.
Pinedo, M. L., 2012. Scheduling: Theory, Algorithms, and Systems, 4th Edition, Springer Science and Business Media, United States.
Petrides, D., Koulouris, A., Siletti, C., Jiménez, J. O., and Lagonikos, P. T., 2010. The Role of Simulation and Scheduling Tools in the Development and Manufacturing of Active Pharmaceutical Ingredients, Chemical Engineering in the Pharmaceutical Industry: R&D to Manufacturing, 521-541.
Ribas, I., Leisten, R., and Framiñan, J. M., 2010. Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective, Computers & Operations Research, 37(8), 1439-1454.
Su, C. T., 2013. Quality Engineering: Off-line Methods and Applications, CRC Press, United States.
Tong, L. I. and Su, C. T., 1997. Optimizing multi-response problems in the Taguchi method by fuzzy multiple attribute decision making. Quality and Reliability Engineering International, 13(1), 25-34.
Yang, T., and Chou, P., 2005. Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method, Mathematics and Computers in Simulation, 68(1), 9-21.
Yang, T., Chen, M. C., and Hung, C. C., 2007. Multiple attribute decision-making methods for the dynamic operator allocation problem, Mathematics and Computers in Simulation, 73(5), 285-299.