多機台流線式製程（Flow shop with multi-processor, FSMP）是指在一流線式（Flow shop）的生產線中，每個工作站皆包含一台（含）以上之機台，雖然在同一工作站中的機台不盡相同，但其功能卻相同，對於所有的產品皆能從事生產，在現實的環境中如：半導體產業、電子產業及石化之相關產業皆是屬於此一類型之製造生產系統。
　　多機台流線式製程之排程屬於複雜性高的問題，不但生產線的行為不容易以簡單的數學模式來表示之外，其求解的難度也相當高。本論文計劃利用離散事件模擬對於複雜行為的模式化能力以及智慧搜尋法的求解能力，期望結合兩者將流線式製程之投料排序及工作站間派工的問題最佳化，以提高整體生產線的表現，最後並以一實際之積層陶瓷電容製造為例，以說明其可行性及應用性。
　　排程與排序是一體兩面的，然而針對單一工作站進行排序即為一複雜的問題，更不用說是多個工作站，而派工法則雖然簡化了排序的難度，卻無法最佳化排程的表現，因此本論文以兩個方向進行多機台流線式製程之排程研究，一為結合模擬與禁忌搜尋法最佳化訂單排序的問題，另一為結合模擬與遺傳演算法最佳化工作站派工組合的問題。經由實驗結果顯示，所提的方法相較於傳統常用的派工法則皆有明顯的改善。然而對於未來要將所提的方法應用在其他產業上時，結合模擬與遺傳演算法最佳化工作站派工組合的作法則在計算時間的考量下是較有效率的。

　　The flow shop with multiple processors (FSMP) scheduling problem involves the sequencing of jobs in a flow shop where, at any stage, there may exist more than one identical machine. A machine can process at most one job at a time and the jobs are subject to precedence constraint that limits them to an identical processing order through all stages of operations. This type of scheduling environment is relatively common and has a variety of applications including semiconductor and electronics manufacturing, and petrochemical production.
　　FSMP scheduling is NP-hard problem. Because the behavior of real environment is complex to be model by mathematical programming and the optimal solution is not easy to be found. The discrete-event simulation is capable of modeling non-linear and stochastic problem. Intelligent search such as tabu search (TS) and genetic algorithm (GA) are proven tools in solving a complex optimization problem. This research proposes to solve the FSMP scheduling problem by combining intelligent search and simulation approach. A case study from multilayer ceramic capacitor (MLCC) manufacturing is used to illustrate the proposed solution methodology.
　　This research was divided into two parts. First one is hybrid tabu search simulation to optimize the job sequence decision problem; the other one is hybrid genetic algorithms simulation to optimize the multi-attribute combinatorial dispatching decision problem. The results shown that the proposed methodologies are outperformance than other methods. However, for extending the problem to other industry, the second proposed method will save more computer time.

白明憲，（民73），生產排程：單機模式之研究，國立政治大學企業管理研究所，碩士論文。
Azadivar, F. and Tompkins, G., 1999, Simulation optimization with qualitative variables and structural model change: A genetic algorithm approach, European Journal of Operational Research, 113, 169-182
Baker, K.R., 1974, Introduction to Sequencing and Scheduling, New York: Wiley.
Balas, E., 1965, An additive algorithm for solving linear programs with zero-one variables, Operations Research, 13, 517-546.
Balas, E., 1967, Discrete programming by the filter method, Operations Research, 15, 915-957.
Barman, S., 1997, Simple priority rule combinations: an approach to improve both flow time and tardiness, International Journal of Production Research, 35, 2857-2870.
Barrett, R.T. and Barman, S., 1986, A SLAMII simulation study of a simplified flow shop, Simulation, 47,181-189.
Ben-Daya, M. and Al-Fawzan, M., 1998, A tabu search approach for the flowshop scheduling problem, European Journal of Operational Research, 109, 88-95.
Botta-Genoulaz, V., 2000, Hybrid flowshop scheduling with precedence constrains and time lags to minimize maximum lateness, International Journal of Production Economics, 64, 101-111.
Brah, S. A., 1992, Complexity of the flow shop with multiple processors scheduling problem, and some dominance conditions, Optimization: Techniques and Applications, Vol. 1, Phua, K.H. et al. (Ed.), World Science, Singapore, 538-545.
Brah, S.A. and Loo, L.L., 1999, Heuristics for scheduling in a flow shop with multiple processors, European Journal of Operational Research, 113, 113-122.
Campbell, H. G., Dudek, R. A., and Smith, M. L., 1970, A heuristic algorithm for the n-job, m-machine sequencing problem, Management Science, 16, B630-B637.
Chambers, L., 1995, Practical handbook of genetic algorithms, CRC, Boca Raton, Florida.
Chang, Y.L., Sueyoshi, T. and Sullivan, R.S., 1996, Ranking dispatching rules by data envelopment analysis in a job shop environment, IIE Transactions, 28, 631-642.
Deal, D. E., Yang, T., and Hallquist, S. J., 1994, Job scheduling in petrochemical production: two-stage processing with finite intermediate storage, Computers and Chemical Engineering, 18, 333-344.
eM-Plant user’s manual, version 4.6.1, 2000, Tecnomatix Technologies, Stuttgart, Germany.
Eshelman, L.J., Caruanu, R.A. and Schaffer, J.D., 1989, Biases in crossover landscape, In Proc. 3rd International Conference Genetic Algorithm, 10-19.
Glover, F., 1989, Tabu Search –PartⅠ, ORSA Journal On Computing, 1, pp 190 – 206.
Glover, F., Kelly, J.P. and Laguna, M., 1999, New advances for wedding optimization and simulation, Proceedings of the 1999 Winter Simulation Conference, Phoenix, USA.
Glover, F. and Kochenberger, G.A., 2003, Handbook of Metaheuristics, Kluwer Acadnic Publishers, London.
Golgberg, D.E., 1989,Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, MA.
Gomory, R.E., 1965, On the relation between integer and non-integer solution to linear programs, Proceedings of the National Academy of Science, 53, 260-265.
Gomory, R.E., 1967, Faces of an integer polyhedron, Proceedings of the National Academy of Science, 57, 16-18.
Grangeon, N., Tanguy, A. and Tchernev, N., 1999, Generic simulation model for hybrid flow-shop, Computers and Industrial Engineering, 37, 207-210.
Gupta, J. N. D., 1988, Two-stage, hybrid flowshop scheduling problem, Journal of the Operational Research Society, 39, 359-364.
Gupta, J. N. D. and Tunc, E. A., 1991, Schedules for a two-stage hybrid flowshop with parallel machines at the second stage, International Journal of Production Research, 29, 1489-1502.
Holland, J., 1975, Adaptation in Natural and Artificial System, University of Michigan Press, Ann Arbor.
Holthaus, O., 1999, Scheduling in job shops with machine breakdowns: an experimental study, Computers and Industrial Engineering, 36, 137-162.
Hopp, W.J. and Spearman, M.L., 2001, Factory Physics, 2nd edition, New York: McGraw-Hill.
Hunsucker, J.L. and Shah J.R., 1992, Performance of priority rules in a due date flow shop, Omega-The International Journal of Management Science, 1, 73-89.
Hunsucker, J.L. and Shah J.R., 1994, Comparative performance analysis of priority rules in a constrained flow shop with multiple processors environment, European Journal of Operational Research, 72, 102-114.
Jayamohan M.S., and Rajendran, C., 2000, A comparative analysis of two different approaches to scheduling in flexible flow shop, Production Planning and Control, 11, 572-580.
Kelton, W.D., Sadowski, R.P. and Sadowski, D.A., 2002, Simulation with Arena, 2nd edition, McGraw-Hill, New York.
Kim, T.D., Kim, J.U., Lim, S.K. and Jun, H.B., 1998, due-date based scheduling and control policies in a multiproduct semiconductor wafer fabrication facility, IEEE Transactions on Semiconductor Manufacturing, 11, 155-164.
Kirkpatrick, S., Gellstt,C.D. and Vecchi, M.P., 1983, Optimization by simulated annealing, Science 220, 671-680.
Kuo, Y., Chang, I. and Yang, T., 2003, A hybrid genetic algorithms and simulation method in solving a parallel machine dispatch problem, Seventh International Conference on Automation Technology, Taiwan.
Lee, H.T., Chen, S.H. and Kang, H.Y., 2002, Multicriteria scheduling using fuzzy theory and tabu search, International Journal of Production Research, 40, 1221-1234.
Linn, R. and Zhang, W., 1999, Hybrid flow shop scheduling: a survey, Computers and Industrial Engineering, 37, 57-61.
Morton, T. E., Pentico, D.W., 1993, Heuristic Scheduling Systems : with Applications to Production System and Project Management, USA: Wiley.
Nawas, M., Enscore, E., Ham, I., 1983, A heuristic algorithm for the m machine, n job flow shop sequence problem, Omega-The International Journal of Management Science, 11, 91-95.
Omwubolu, G.C. and Mutingi, M., 2001, Optimizing the multiple constrained resources product mix problem using genetic algorithms, International Journal of production Research, 39, 1897-1910.
Osman, I.H., Metastratesy Simulated Annealing and Tabu Search Algorithms for Combinatorial Optimization Problems, United Kingdom : Philosophy of the University of London.
Pinedo, M., 1995, Scheduling: Theory, Algorithms, and Systems, Englewoog Cliffs, New Jersey: Prentice Hall.
Pongcharoen, P., Hicks, C., Braiden, P.M. and Stewardson, D.J., 2002, Determining optimum Genetic Algorithm parameters for scheduling the manufacturing and assembly of complex products, International Journal of Production Economics, 78, 311-322.
Petroni, A. and Rizzi, A., 2002, A fuzzy logic based methodology to rank shop floor dispatch rules, International Journal of Production Economics, 76, 99-108.
Proust, C., Gupta, J. N. D., Deschamps, V., 1991, Flowshop scheduling with set-up, processing and removal times separated, International Journal of Production Research, 29, 479-493.
Rajendran, C. and Holthaus, O., 1999, A comparative study of dispatching rules in dynamic flowshops and jobshops, European Journal of Operational Research, 116, 156-170.
Salvador, M. S., 1973, A solution to a special case of flow-shop scheduling problems. In S.E. Elmaghraby (ed.), Symposium of the Theory of Scheduling and Applications (New York: Springer-Verlag), pp. 83-91
Santos, D. L., Hunsucker, J. L. and Deal, D. E., 1996, An evaluation of sequencing heuristics in flow shops with multiple processors, Computers and Industrial Engineering, 30, 681-692.
Sarper, H. and Henry, M.C., 1996, Combinatorial evaluation of six dispatching rules in a dynamic two-machine flow shop, Omega-The International Journal of Management Science, 24, 73-81.
Syswerda, G., 1989, Uniform crossover in genetic algorithm, In Proc. 3rd International Conference Genetic Algorithm, 2-9.
Tompkins, J. A., White, J. A., Bozer, Y. A., Frazelle, E. H., Tanchoco, J. M. A., and Trevino, J., 1996, Facilities Planning, 2nd edition, New York: Wiley.
Townsend, W., 1977, Sequencing n-jobs on m-machines to minimize maximum tardiness: A branch-and-bound solution, Management Science, 23, 1016-1019.
Uetake, T., Tsubone, H. and Ohba, M., 1995, A production scheduling system in a hybrid flow shop, International Journal of Production Economics, 41, 395-398.
Vinicius, A.A. and Debora, P.R., 1999, Tabu search for tardiness minimization in flowshop scheduling problems, Computers & operations Research, 26, 219-235.
Wilkerson, L.J. and Irwin, J.D., 1971, An improved algorithm for scheduling independent tasks, AIIE Transactions, 3, 239-245.
Yang, T. and Tseng, L., 2002, Solving a multi-objective simulation model using a hybrid response surface method and lexicographical goal programming approach-a case study on integrated circuit ink-marking machines. Journal of the Operational Research Society, 53, 211-221.
Yang, T., Rajasekharan, M. and Peters, B. A., 1999, Semiconductor fabrication facility design using a hybrid search methodology, Computers & Industrial Engineering, Vol. 36, pp 565-583.