本研究針對半導體封裝廠的機台及人員配置規劃進行探討，由於市場對於產品需求的變化下，必需時常購買更新機台來因應，而在新舊機台、不同型號的機台同時並存的情況下，該如何依照客戶需求分配產品，並讓機台利用率能最大化，產出量也能最大，因此在考慮兩目標同時牽制互相影響的情況下，解決機台及人員配置問題。
故本研究以半導體封裝業之瓶頸站，黏晶(Die bound)、焊線(Wire bound)及模壓(Molding)三站，並考量客戶有指定型號的機台、機台也有優先使用順序的問題、生產作業時間也會依照不同的產品而有不同的變化，以模擬最佳化的方法同時考慮兩個目標，並尋求機台及人員配置方案。由於本研究考量機台數、人員數的問題所以搜索空間較大，因此利用多目標粒子群最佳化演算法進行搜尋，並根據演算法模擬計算後所得到的結果與多目標基因演算法、多目標差分進化演算法進行演算法比較，發現多目標粒子群最佳化演算法所得到的結果優於其他兩種演算法，並透過多目標粒子群最佳化演算法結合模擬最佳化的方法給予個案公司一個有效的機台、人員配置組合的參考基準。

In order to fulfill the demand, manufacture should have sufficient resources including the machines and operators. However, having too many resources will reduce the efficiency as well as increase the fix cost. This study proposes an algorithm to optimize the manufacturers’ resources including machines and operators to fulfil the demand. The proposed algorithm applies a non-dominated sorting particle swarm optimizer algorithm (NSPSO). Herein, a multi-objective algorithm is employed because there are two objectives used in this study. They are utilization and throughput. Both of them should be maximized. This study applies the proposed algorithm to a semiconductor IC-packaging factory. This factory has unique designed model of machine for each customer causing different priority and production time. During the operations, the bottleneck stations in this factory are the die bound, wire bound, and molding stations. This problem is serious due to high variety of demands. Therefore, it is very important to find the optimal number of machines and operators to fulfill the demands as well as maximizing utilization and throughput.
This study simulates the proposed algorithm using Flexim. For evaluation, simulation results obtained by NSPSO algorithm are compared with non-dominated sorting genetic (NSGAII) algorithm and non-dominated sorting differential evolution (NSDE) algorithm. The results show that the result obtained by NSPSO algorithm is better than those of NSGAII and NSDE algorithms.

Al-Hawari, T., Ali, M., Al-Araidah, O., & Mumani, A. (2015). Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach. The International Journal of Advanced Manufacturing Technology, 77(5), 1419-1432. doi:10.1007/s00170-014-6545-5
Amaran, S., Sahinidis, N. V., Sharda, B., & Bury, S. J. (2016). Simulation optimization: a review of algorithms and applications. 4OR, 12(4), 301-333.
Bang, J. Y., & Kim, Y. D. (2010). Hierarchical Production Planning for Semiconductor Wafer Fabrication Based on Linear Programming and Discrete-Event Simulation. IEEE Transactions on Automation Science and Engineering, 7(2), 326-336.
Banks, J., CARSON II, J. S., & Barry, L. (2005). Discrete-event system simulationfourth edition: Pearson.
Bretthauer, K. M. (1995). Capacity planning in networks of queues with manufacturing applications. Mathematical and Computer Modelling, 21(12), 35-46.
Carson, Y., & Maria, A. (1997). Simulation optimization: methods and applications. Paper presented at the Proceedings of the 29th conference on Winter simulation, Atlanta, Georgia, USA.
Chaari, T., Chaabane, S., Loukil, T., & Trentesaux, D. (2011). A genetic algorithm for robust hybrid flow shop scheduling. International Journal of Computer Integrated Manufacturing, 24(9), 821-833.
Chang, X., Dong, M., & Yang, D. (2013). Multi-objective real-time dispatching for integrated delivery in a Fab using GA based simulation optimization. Journal of Manufacturing Systems, 32(4), 741-751.
Chaudhry, I. A., & Drake, P. R. (2008). Minimizing total tardiness for the machine scheduling and worker assignment problems in identical parallel machines using genetic algorithms. The International Journal of Advanced Manufacturing Technology, 42(5), 581.
Chen, & Wang, C.-C. (2016). Multi-objective simulation optimization for medical capacity allocation in emergency department. Journal of Simulation, 10(1), 50-68.
Chen, J. C., Chen, T.-L., Pratama, B. R., & Tu, Q.-F. (2016). Capacity planning with ant colony optimization for TFT-LCD array manufacturing. Journal of Intelligent Manufacturing, 1-19.
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE transactions on evolutionary computation, 8(3), 256-279.
Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms: John Wiley \& Sons, Inc.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Eberchart, R., & Kennedy, J. (1995). Particle swarm optimization. Paper presented at the IEEE International Conference on Neural Networks, Perth, Australia.
Fonseca, C. M., & Fleming, P. J. (1993). Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization. Paper presented at the Icga.
Fukuyama, Y. (2008). Fundamentals of particle swarm optimization techniques. Modern heuristic optimization techniques: theory and applications to power systems, 71-87.
Gordon, G. (1977). System Simulation: Prentice Hall PTR.
Guo, C., Zhibin, J., Zhang, H., & Li, N. (2012). Decomposition-based classified ant colony optimization algorithm for scheduling semiconductor wafer fabrication system. Computers & Industrial Engineering, 62(1), 141-151.
Hamta, N., Fatemi Ghomi, S. M. T., Jolai, F., & Akbarpour Shirazi, M. (2013). A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect. International Journal of Production Economics, 141(1), 99-111.
Hisao, I., Noritaka, T., & Yusuke, N. (2008, 1-6 June 2008). Evolutionary many-objective optimization: A short review. Paper presented at the 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence: MIT Press.
Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Paper presented at the Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on.
Iorio, A. W., & Li, X. (2005). Solving Rotated Multi-objective Optimization Problems Using Differential Evolution. In G. I. Webb & X. Yu (Eds.), AI 2004: Advances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004. Proceedings (pp. 861-872). Berlin, Heidelberg: Springer Berlin Heidelberg.
Jeong, S. J., Lim, S. J., & Kim, K. S. (2006). Hybrid approach to production scheduling using genetic algorithm and simulation. The International Journal of Advanced Manufacturing Technology, 28(1), 129-136.
Jiang, S., Ong, Y.-S., Zhang, J., & Feng, L. (2014). Consistencies and contradictions of performance metrics in multiobjective optimization. IEEE Transactions on Cybernetics, 44(12), 2391-2404.
Knowles, J., & Corne, D. (1999). The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. Paper presented at the Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on.
Law, A. M., Kelton, W. D., & Kelton, W. D. (1991). Simulation modeling and analysis (Vol. 2): McGraw-Hill New York.
Li, X. (2003). A non-dominated sorting particle swarm optimizer for multiobjective optimization. Paper presented at the Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI, Chicago, IL, USA.
Li, X., Yalaoui, F., Amodeo, L., & Chehade, H. (2012). Metaheuristics and exact methods to solve a multiobjective parallel machines scheduling problem. Journal of Intelligent Manufacturing, 23(4), 1179-1194.
Lin, J. T., & Chen, C.-M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing. Simulation Modelling Practice and Theory, 51, 100-114.
Lin, J. T., Chen, C.-m., Chiu, C.-c., & Fang, H.-y. (2013). Simulation optimization with PSO and OCBA for semiconductor back-end assembly. Journal of Industrial and Production Engineering, 30(7), 452-460.
Lin, J. T., & Huang, C.-J. (2014). A simulation-based optimization approach for a semiconductor photobay with automated material handling system. Simulation Modelling Practice and Theory, 46, 76-100.
Lin, J. T., Sir, M. Y., & Pasupathy, K. S. (2013). Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services. Omega, 41(5), 881-892.
Nait Tahar, D., Yalaoui, F., Chu, C., & Amodeo, L. (2006). A linear programming approach for identical parallel machine scheduling with job splitting and sequence-dependent setup times. International Journal of Production Economics, 99(1–2), 63-73.
Nayani, N., & Mollaghasemi, M. (1998, 13-16 Dec 1998). Validation and verification of the simulation model of a photolithography process in semiconductor manufacturing. Paper presented at the 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).
Nearchou, A. C. (2011). Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization. International Journal of Production Economics, 129(2), 242-250.
Pegden, C. D., Sadowski, R. P., & Shannon, R. E. (1995). Introduction to Simulation Using SIMAN: McGraw-Hill, Inc.
Perkgoz, C., Azaron, A., Katagiri, H., Kato, K., & Sakawa, M. (2007). A multi-objective lead time control problem in multi-stage assembly systems using genetic algorithms. European Journal of Operational Research, 180(1), 292-308.
Pidd, M. (1988). Computer simulation in management science.
Price, K., Storn, R. M., & Lampinen, J. A. (2005). Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series): Springer-Verlag New York, Inc.
Qiao, F., Ma, Y. m., Li, L., & Yu, H. x. (2013). A Petri Net and Extended Genetic Algorithm Combined Scheduling Method for Wafer Fabrication. IEEE Transactions on Automation Science and Engineering, 10(1), 197-204.
Ramezanian, R., & Ezzatpanah, A. (2015). Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem. Computers & Industrial Engineering, 87, 74-80.
Rudolph, G. (1999). Evolutionary Search under Partially Ordered Fitness Sets.
Shannon, R., & Johannes, J. D. (1975). Systems simulation: the art and science. IEEE Transactions on Systems, Man, and Cybernetics, 6(10), 723-724.
Sreedhar, D. (2013). Differential evolution based multiobjective optimization-A review. International Journal of Computer Applications, 63(15).
Srinivas, N., & Deb, K. (1994). Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 2(3), 221-248.
Storn, R., & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341-359.
Weigert, G., Klemmt, A., & Horn, S. (2009). Design and validation of heuristic algorithms for simulation-based scheduling of a semiconductor backend facility. International Journal of Production Research, 47(8), 2165-2184.
Ying, T., MengChu, Z., & Qiu, R. G. (2003). Virtual production lines design for back-end semiconductor manufacturing systems. IEEE Transactions on Semiconductor Manufacturing, 16(3), 543-550.
Zeinali, F., Mahootchi, M., & Sepehri, M. M. (2015). Resource planning in the emergency departments: A simulation-based metamodeling approach. Simulation Modelling Practice and Theory, 53, 123-138.
Zhang, W., Xu, W., & Gen, M. (2013). Multi-objective Evolutionary Algorithm with Strong Convergence of Multi-area for Assembly Line Balancing Problem with Worker Capability. Procedia Computer Science, 20, 83-89.
Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2), 173-195.
Zitzler, E., & Thiele, L. (1998). Multiobjective optimization using evolutionary algorithms—a comparative case study. Paper presented at the International Conference on Parallel Problem Solving from Nature.