彈性製造系統系統(Flexible Manufacturing System, FMS)中除了機台資源外還包含搬運系統如自動物料搬運車輛(Automatic guided vehicles, AGV)，而車輛搬運時會導致機台閒置，因此在本研究目標是要同步(Simultaneous)處理作業排程與車輛排程，使總完工時間(Makespan)最小化。考量作業排程與車輛排程是一複雜的NP-Hard問題，本研究將利用粒子群演算法(Particle Swarm Optomization)結合粒子位置交換機制(Muti-type individual enhancement scheme)產生作業排程，並且與車輛排程演算法結合替每項加工作業選擇搬運車。
文獻中求解同時排程問題多以數學規劃法，車輛搬運工件的時間是以搬運距離除以車速，未考慮到車輛在途中可能因為壅塞而延遲搬運時間或是發生車輛鎖死(deadlock)，因此本研究加入車輛區域控制(zone-control)。由於FMS中，機台為多功能機台，工件的同一作業可選擇替代路徑與機台，此特性增加排程的複雜性。同時比較基礎模型與兩個延伸模型，觀察simple model 與 complex model之差異。另外，在現實的FMS中不同工件的作業時間有變異性，因此考量工件加工時間具有隨機性，由於模型具有隨機因子，若只模擬一次實驗隨機性質所造成的誤差值可能會影響判斷，若模擬太多次則會浪費時間成本。因此本研究OCBA(Optimal Computing Budget Allocation)適當的分配模擬資源給予無法分辨出優劣的方案或變異太大的方案，以最少的模擬資源找出最佳方案，結果顯示可以降低65%的模擬資源。

目錄
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 5
1.3研究範圍 6
1.4研究步驟與方法 6
第二章 文獻回顧 9
2.1排程問題 9
2.2 FMS同步機台與車輛排程(Simultaneous Scheduling) 13
2.3 區域控制(zone-control) 16
2.4替代機台(Alternative Machine) 17
2.5 模擬最佳化 18
2.5.1 粒子群聚演算法 20
2.5.2 Optimal Computing Budget Allocation(OCBA) 24
第三章 彈性製造系統之機台與車輛排程問題 26
3.1 FMS同步機台與車輛排程問題 26
3.2 問題描述 27
3.2.1 基礎模型 27
3.2.2延伸模型-1 27
3.2.3延伸模型-2 28
3.3問題定義與假設 29
第四章 研究方法論 32
4.1 模擬最佳化架構 32
4.2 粒子群最佳化演算法 34
4.2.1 決定初始粒子位置(Encoding and Decoding)及初始化位置 34
4.2.2 評估適應值函數 37
4.2.3 更新自體最佳解pbest與群體最佳解gbest 41
4.2.4 執行Muti-type individual enhancement scheme 41
4.2.5更新速度與位置 44
4.2.6 終止條件 45
4.3 粒子群最佳化演算法結合OCBA 46
第五章 模擬實驗與分析 52
5.1 模擬模式建構 52
5.1.1 模擬模式建構目的 52
5.1.2 模擬模式範圍與細緻度 52
5.1.3 模擬模式建構 55
5.2 實驗一：模型確認與simple model versus complex model 58
5.2.1探討加入區域控制 59
5.2.2探討加入替代機台 62
5.2.3 實驗一小結 69
5.3 實驗二：確定型模擬模式與實驗 69
5.3.1 使用粒子群聚演算法求解同步排程問題 69
5.3.2 在FMS加入區域控制應用於同步機台與車輛排程 76
5.3.3 在FMS加入替代機台應用於同步機台與車輛排程 82
5.4 實驗三：隨機型模擬模式與實驗 89
5.4.1 探討加工時間具有隨機性 89
5.4.2 OCBA與Equal Allocation模擬效率比較 91
5.4.3 PSO+OCBA參數分析 94
5.4.4 考慮隨機性之實驗結果 103
5.4.5 確定型模型(simple model) versus 隨機型模型(complex model) 111
5.5 實驗結論 112
5.5.1 實驗一：模型確認與simple model versus complex model 112
5.5.2 實驗二：確定型模擬模式與實驗 113
5.5.3 實驗三：隨機型模擬模式與實驗 115
第六章 結論與建議 119
6.1 結論 119
6.2 建議 122
參考文獻 123
附錄 127

參考文獻
[1]王治平，”實際零工式生產排程問題的派工法則”，政治大學資訊管理研究所 碩士論文，2003。
[2]王建榮，”移動式區域控制無人搬運車系統”，中央大學工業管理研究所碩士論文，1999。
[3]吳進富，”在具製程與途程彈性之AGV控制策略”，中央大學工業管理研究所碩士論文，1999。
[4]張祐翔，”應用模擬最佳化於FMS之機台與車輛同步排程”，清華大學工業工程與工程管理所碩士論文，2013。
[5] 黎士賢，”網路式移動區域控制無人搬運車系統”，中央大學工業管理研究所碩士論文，1999。
[6]Abdelmaguid, T. F.,Nasef, A. O., Kamal, B. A. and Hassan, M. F., “A hybrid GA/Heuristic approach to the simultaneous scheduling of machines and automated guided vehicles”, International Journal of Production Research, Vol.42, No.2, 2004, pp267-281.
[7]Anwar, M. F. and Nagi, R., “Intergrated scheduling of material handling and manufacturing activities for JIT production of complex assemblies”, International Journal of Production Research, Vol.36, No.3, 1993, pp653-681.
[8]Babu, A. G., Jerald, J., Haq, A. N., Luxmi, V. M., Vigneswaralu, T. P., “Scheduling of machines and automated guided vehicles in FMS using differential evolution”, International Journal of Production Research, Vol.48, No.16, 2010, pp4683-4699.
[9]Bilge, U. and Ulusoy, G., “A time windows approach to simultaneous scheduling of machines and material handling system in an FMS”, Journal of Operations Research, Vol.43, No.6, 1995, pp1058-1070.
[10]Chen, C. H., Lee, L. H., “Stochastic Simulation Optimization an Optimal Computing Budget Allocation”, System Engineering and Operations Research, Vol.1, 2010.
[11]Conway, R. W., Maxwell, W. L., and Miller, L. W., Theory of Scheduling, Addison Wesley, Reading Massachusetts., USA, 1967
[12]Fanti, M. P., “Event-based controller to avoid deadlock and collisions in zone-control AGVs”, International Journal of Production Research, Vol.40, No.6, 2002, pp1453-1478.
[13]Fernández, J., Besuievsky, G., “Inverse lighting design for interior buildings integrating natural and artificial sources”, Computers & Graphics, Vol 36, No.8, 2012, pp1096-1108.
[14]Groover, M. P., Automation, Production Systems, and Computer Integrated Manufacturing, Printed-Hall of India, New Delhi, 1996.
[15]Han, H. M., McGinnis, L. F., “Control of material handling transporter in automated manufacturing”, IIE Transactions, Vol.21, No.2, 1989, pp184-190.
[16]Hansen, P., & Mladenivic, N., “Variable neighborhood search: Principles and application”, European Journal of Operation Research, Vol.130, No.3, 2001, pp449-467.
[17]Harmonosky, C. M., “Simulation-based real-time scheduling：review of recent developments”, Procedings of the 1995 Winter Simulation Conference, 1995, pp220-224.
[18]Ilkyeong, M., Lee, J., “Genetic Algorithm Application to the Job Shop Scheduling Problem with Alternative Routing”,BK21 Logistics Team, Industrial Engineering, Pusan National University, 2000, pp1-99.
[19]Jerald, J., Asokan, P., Saravanan, P., Rani, D. C., “Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm”, International Journal Advanced Manufacturing Technology, 29, 2006, pp584-589.
[20]Karabuk, S., and SabunChoglu, I., “A beam search based algorithm for scheduling machines and AGVs in an FMS”, Proc. Second Industrial Engg. Res. Conf, 1993, pp308-312.
[21]Kumar, M. V. S., Janardhana, R., Rao, CSP., “Simultaneous scheduling of machines and vehicles in an FMS　environment with alternative routing”, International Journal Advanced Manufacturing Technology, Vol.53, No.1-4, 2011, pp339-351.
[22]Kuo, I. H. , Horng, S. J., Kao, T. W. , Lin, T. L., Lee, C. L., Terano, T. , Pan, Y., “An efficient flow-shop scheduling algorithm based on a hybrid particle swarm optimization model”, Expert Systems with Application Vol.36, No.3, 2009, pp7027-7032.
[23]Lacomme, P., Moukrim, A., Tchernev, N., “Simultaneous job input sequencing and vehicle dispatching in a single-vehicle automated guide vehicle system：a heuristic branch-and-bound approach coupled with a discrete events simulation model”, International Journal of Production Research, Vol.43, No.9, pp1911-1942.
[24]Lin, T. L. , Horng, S. J. , Kao, T. W., Chen, Y. H. , Run, R. S. , Chen, R. J. , Lai, J. L. , Kuo, I. H., “An efficient job-shop scheduling algorithm based on particle swarm optimization”, Expert Systems with Application, Vol.37, No.3, 2010, pp2629-2636
[25]Muhammad H. F. M. F., Murata, T., “Makespan Minimization of Machines and Automated Guided Vehicles Schedule Using Binary Particle Swarm Optimization”, Proceedings of the International MultiConference of Enginners and Computer Scientists,2010
[26]Nasr, N. and Elsayed, E. A., “Job Shop with Alternative Machines”, International Journal of Production Research, Vol.28, No.9, 1990, pp1595-1609.
[27]Parthasarthy, S., Rajendran, C., “A simulated annealing heuristic for scheduling to minimize weighted tardiness in a flowshop with sequence dependent setup times of jobs-a case study”, Prodouction Planning & Control, Vol.8,No.5, 1997, pp475-485.
[28]Raman, N., Talbot, F. B., Rachamadugu, R. V., “Simultaneous scheduling machines and material handling devices in automated manufacturing”, Proceedings of second ORSA/TIMS Conference on Flexible Manufacturing System,1986, pp455-466.
[29]Reddy, B. S. P. and Rao, C. S. P., “A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS”, International Journal of Advanced Manufacturing Technology, Vol.31, No.5-6, 2006, pp602-613.
[30]Erola, R., Sahina, C., Baykasoglub, A., Kaplanogluc, V., “A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems”, Applied Soft Computing, Volume 12, No.6, 2012, pp1720-1732.
[31]Satishkumar, M. V., “Simultaneous scheduling of machines and Agvs using evolutionary optimization algorithms”, PhD Thesis, Jawaharlal Nehru Technological University, 2011.
[32]Stecke, K. E., “Design, planning, scheduling, and control problems of flexible manufacturing systems”, Annals of Operations Research, Vol.3, No.1, 1985, pp.1-13.
[33]Subbaiah, K. V., Rao, M. N. and Rao, K.N., “Scheduling of AGVs and machines in FMS with makespan criteria using sheep flock heredity algorithm”, International journal of Physical Sciences, Vol.4, No.2, 2009, pp139-148.
[34]Ulusoy, G., and Bilge, U., “Simultaneous Scheduling of machines and automated guided vehicles”, International Journal of Production Research, Vol.31, No.12, 1993, pp2857-2873.
[35]Ulusoy, G., Srvnkaya, F. – Serifogulg, Umit, Bilge, “A Genetic algorithm approach to the simultaneous scheduling of machines and automated guided vehicles”, Computers & Operations Research, Vol.24, No.4, 1997, pp335-351.
[36]Vis, I., “Survey of Research in the Design and Control of Automated Guided Vehicle Systems”, European J. of Operational Res., Vol.170, No.3, 2006, pp677-709.
[37]Wang, F. K. and Lin, J. T., “Performance evalution of an automated material handling system for a wafer fab”, Robotics and Computer-Integrated Manufacturing, Vol. 20, No.2, 2004, pp91-100.
[38]Wilhelm, W. and Shin, H. “Effectiveness of alternative operations in a flexible manufacturing system”, International Journal of Production Research, Vol.23, No.1, 1985, pp65-79.
[39]Xia,W.J, Wu, Z.M, “An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems”, Computers & Industrial Engineering Vol.48, No.2, 2005, pp 409–425
[40]Yeh, M.S., Yeh, W.C., “Deadlock prediction and avoidance for zone-control AGVs”, International Journal of Production Research, Vol.36, No.10, 1998, pp2879-2