隨著消費性產品市場需求模式的改變，消費者追求物美價廉且客製化的產品。傳統的製造策略主要是追求經濟規模，面對市場需求變動快與產品生命週期短的改變，傳統生產系統的集中和分級控制的結構設計，已無法滿足複雜且變動迅速的製造環境，並有效支援因產品多樣化與複雜化而衍生的生產製造問題。
因應產品需求變化與複雜產品線衍生的生產規劃、排程規劃、製造資源配置等問題，本研究發展以決策為基礎之智能製造系統架構，建構具備穩定與效率的製造系統模組，以及具備決策能力的代理人系統。其中，第一部分是階層式的製造系統，追求穩定與效率，以達生產最佳化。從客戶實際訂單與潛在需求開始，透過訂單管理模組彙整，決定總需求量，作為後續生產規劃模組、多目標排程規劃模組的依據；進而展開至製造現場的資源配置與派工，以及機台的智能整合感控功能，即時取得產品生產資訊與機台狀況，掌握生產進度。第二部分則是具備可適性、靈活性、協同自主性與決策能力的代理人製造系統。透過代理人的自主協同溝通，在突發事件發生時，回饋最新的即時生產資訊；並提出解決方案，賦予智能製造系統決策能力，處理干擾變異事件，即時將系統回歸正常運作。
本研究利用三個實證研究驗證效度：第一個為TFT-LCD模組組裝多目標排程問題，考量實際面板產業的後段模組組裝線生產限制，利用多目標基因演算法找出滿足多目標的最佳妥協排程；第二個是TFT-LCD模組組裝的塗膠製程變異造成的生產擾動案例。在異常發生時，啟動決策代理人的異常處理機制。透過代理人之間的資訊交換，即時了解問題狀況，並提出解決方案給予決策者參考，選出最適方案作為異常處理的生產決策。第三個為半導體8吋晶圓代工廠的人工晶圓搬運系統之人員配置問題，透過本研究發展的人員配置模式與搬運途程規劃，提出最佳人力配置，提升人員生產力。

Consumers pursue affordable and customized products and services with the changes of consumer products market. Manufacturing industry is facing rapid changes in market demand, short product life cycle, fast technology migration and high capital investment, resulting in difficult to accurately predict demand, lengthen production lead time, and low production yield and other operational challenges.Traditional manufacturing strategy is focusing on scale economies. Facing the changes of various marketing demands and short product life cycles, traditional production system has been unable to meet the complex and rapid changes in the manufacturing environment, and can not effectively deal with derived manufacturing problems.
In order to cope with production planning, scheduling, and manufacturing allocation problems that caused by various product demands and complex product lines. This study aims to develop a framework for decision-based intelligent manufacturing system that including production system modules and decision agent system to pursue quickly response, high productivity, flexible and robust production.
First, the proposed hierarchical manufacturing system is pursuing stability and efficiency to achieve production optimization. The order management module will accumulate all customers' forecast and orders to integrate total demands that as a follow-up demand input for production planning module and multiobjective scheduling module. Further, the manufacturing resource allocation module and cyber-physical system function module will provide promptly manufacturing and machine information to control production status.
Second, the proposed decision-based manufacturing agent has the capability of adaptive, flexibility, coordination, decision-making to provide promptly production while abnormal events arise. The decision-based manufacturing agent could coordinate and communicate with others function modules and agents to integrate information and provide possible solutions for those disturb events. Thus, the decision maker could consult the possible solutions and do the final decision to access those disturb events, and let the production system return to normal operation.
Three empirical cases are demonstrated to validate the proposed decision-based intelligent manufacturing system in this study. First, a multiobjective scheduling problem in TFT-LCD module assembly process is proposed that consider manufacturing conditions in shop floor. The multiobjective scheduling module used multi-objective hybrid genetic algorithm (MO-HGA) to solve multiple and conflicting production objectives, minimizing makespan, minimizing the weighted number of tardy jobs, and minimizing the total machine setup time, that are directly related to productivity and customer satisfaction. Second, a manufacturing disturb case in TFT-LCD module assembly process. When an abnormal event happened in tuffy glue coating process, decision agent was trigged that communicated with other agents to exchange information and understand the latest status. Then, decision agent could provide the possible solutions for decision makers for final judge. Third, a manpower allocation and route planning problem for manual material handling system (MMHS) in a 200mm wafer fab is proposed. The manufacturing resource allocation module used a novel route planning approach to utilize the routes that reduce the technician traveling distance and transportation time. And it also used manpower loading evaluation model is developed for determining the appropriate number of technicians that all improve the MMHS efficiency and productivity.

周哲維、簡禎富 (2016)，需求不確定性之產能擴充決策分析－TFT-LCD產業之個案研究，管理與系統，23卷1期，頁1-29。
張建一、王寶苑 (2014)，迎戰第4次工業革命，產業雜誌，http://www.cnfi.org.tw/kmportal/front/bin/cglist.phtml?Category=100246。
溫于平、簡禎富、周哲維 (2014)，線性規劃與產能配置策略－以友達光電為例，產業工程與管理個案，頁63-94。
簡禎富 (2005)，決策分析與管理，雙葉書廊，台北。
簡禎富、林國義、許鉅秉、吳政鴻 (2016)，台灣生產與作業管理之相關期刊文獻回顧與前瞻：從工業3.0到工業3.5，管理學報，33卷1期，頁87-103。
Agrawal, G. K. and S. S. Heragu (2006), “A survey of automated material handling systems in 300-mm SemiconductorFabs,” IEEE Transactions on Semiconductor Manufacturing, vol. 19, no. 1, pp. 112-120.
Akanle, O. M. and D. Z. Zhang (2008), “Agent-based model for optimising supply-chain configurations,” International Journal of Production Economics, vol. 115, no. 2, pp. 444-460.
Allahverdi, A., C. T. Ng, T. C. E. Cheng, and M. Y. Kovalyov (2008), “A survey of scheduling problems with setup times or costs,” European Journal of Operational Research, vol. 187, no. 3, pp. 985-1032.
Asef-Vaziri, A., N. G. Hall, and R. George (2008), “The significance of deterministic empty vehicle trips in the design of a unidirectional loop flow path,” Computers & Operations Research, vol. 35, no. 5, pp. 1546-1561.
Babiceanu, R. and F. F. Chen (2006), “Development and Applications of Holonic Manufacturing Systems: A Survey,” Journal of Intelligent Manufacturing, vol. 17, no. 1, pp. 111-131.
Bahaji, N. and M. E. Kuhl (2008), “A simulation study of new multi-objective composite dispatching rules, CONWIP, and push lot release in semiconductor fabrication,” International Journal of Production Research, vol. 46, no. 14, pp. 3801-3824.
Barata, J., L. Camarinha-Matos, and G. Cândido (2008), “A multiagent-based control system applied to an educational shop floor,” Robotics and Computer-Integrated Manufacturing, vol. 24, no. 5, pp. 597-605.
Bartlett, K., J. Lee, S. Ahmed, G. Nemhauser, J. Sokol, and B. Na (2014), “Congestion-aware dynamic routing in automated material handling systems,” Computers & Industrial Engineering, vol. 70, pp. 176-182.
Bellman, R. E. and L. A. Zadeh (1970), “Decision-Making in a Fuzzy Environment,” Management Science, vol. 17, no. 4, pp. B-141-B-164.
Bilge, Ü. and J. M. A. Tanchoco (1997), “AGV systems with multi-load carriers: Basic issues and potential benefits,” Journal of Manufacturing Systems, vol. 16, no. 3, pp. 159-174.
Bitar, A., S. Dauzère-Pérès, C. Yugma, and R. Roussel (2014), “A memetic algorithm to solve an unrelated parallel machine scheduling problem with auxiliary resources in semiconductor manufacturing,” Journal of Scheduling, pp. 1-10.
Bongaerts, L., L. Monostori, D. McFarlane, and B. Kádár (2000), “Hierarchy in distributed shop floor control,” Computers in Industry, vol. 43, no. 2, pp. 123-137.
Borangiu, T., P. Gilbert, N.-A. Ivanescu, and A. Rosu (2009), “An implementing framework for holonic manufacturing control with multiple robot-vision stations,” Engineering Applications of Artificial Intelligence, vol. 22, no. 4–5, pp. 505-521.
Branke, J., K. Deb, K. Miettinen, and R. Slowiński (2008), Multiobjective optimization: Interactive and evolutionary approaches, Germany: Springer.
Bratman, M. (1987), Intention, plans, and practical reason, MA: Harvard University Press.
Cakici, E. and S. J. Mason (2007), “Parallel machine scheduling subject to auxiliary resource constraints,” Production Planning & Control, vol. 18, no. 3, pp. 217-225.
Chang, K.-H., Y.-H. Huang, and S.-P. Yang (2013), “Vehicle Fleet Sizing for Automated Material Handling Systems to Minimize Cost Subject to Time Constraints,” IIE Transactions.
Chen, M., J. Wan, and F. Li (2012), “Machine-to-Machine Communications,” KSII Transactions on Internet and Information Systems, vol. 6, no. 2, pp. 480-497.
Chen, S.-H., C.-K. Chang, and C.-L. Kuo (2005), “Optimal scheduling in color filter manufacturing,” Journal of the Chinese Institute of Industrial Engineers, vol. 22, no. 4, pp. 301-308.
Chen, S. L. and M. M. Tseng (2005), “Defining Specifications for Custom Products: A Multi-Attribute Negotiation Approach,” CIRP Annals - Manufacturing Technology, vol. 54, no. 1, pp. 159-162.
Chen, W. C., N. Chiou, J. Lu, J. Yeh, and J. Kuan. (2008). Manual Material Handling System. Paper presented at the 2008 International Symposium on Semiconductor Manufacturing (ISSM), Tokyo, Japan.
Chiang, T. C., Y. S. Shen, and L. C. Fu (2008), “A new paradigm for rule-based scheduling in the wafer probe centre,” International Journal of Production Research, vol. 46, no. 15, pp. 4111-4133.
Chien, C.-F. and C.-H. Chen (2007a), “A novel timetabling algorithm for a furnace process for semiconductor fabrication with constrained waiting and frequency-based setups,” OR Spectrum, vol. 29, no. 3, pp. 391-419.
Chien, C.-F. and C.-H. Chen (2007b), “Using genetic algorithms (GA) and a coloured timed Petri net (CTPN) for modelling the optimization-based schedule generator of a generic production scheduling system,” International Journal of Production Research, vol. 45, no. 8, pp. 1763-1789.
Chien, C.-F., Y.-J. Chen, and C.-Y. Hsu (2015), “A novel approach to hedge and compensate the critical dimension variation of the developed-and-etched circuit patterns for yield enhancement in semiconductor manufacturing,” Computers & Operations Research, vol. 53, pp. 309-318.
Chien, C.-F., Y.-J. Chen, C.-Y. Hsu, and H.-K. Wang (2014a), “Overlay Error Compensation Using Advanced Process Control With Dynamically Adjusted Proportional-Integral R2R Controller,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 2, pp. 473-484.
Chien, C.-F., Y.-J. Chen, and J.-T. Peng (2010), “Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle,” International Journal of Production Economics, vol. 128, no. 2, pp. 496-509.
Chien, C.-F., C.-W. Chou, and H.-C. Yu (2016), “A Novel Route Selection and Resource Allocation Heuristic to Improve the Efficiency of Manual Material Handling System in 200-mm Wafer Fabs for Industry 3.5,” IEEE Transactions on Automation Science and Engineering.
Chien, C.-F. and S.-C. Chuang (2014), “A Framework for Root Cause Detection of Sub-Batch Processing System for Semiconductor Manufacturing Big Data Analytics,” IEEE Transactions on Semiconductor Manufacturing, vol. 27, no. 4, pp. 475-488.
Chien, C.-F., C.-Y. Hsu, and C.-W. Hsiao (2012), “Manufacturing intelligence to forecast and reduce semiconductor cycle time,” Journal of Intelligent Manufacturing, vol. 23, no. 6, pp. 2281-2294.
Chien, C.-F., J.-N. Zheng, and Y.-J. Lin (2014b), “Determining the operator-machine assignment for machine interference problem and an empirical study in semiconductor test facility,” Journal of Intelligent Manufacturing, vol. 25, no. 5, pp. 899-911.
Chou, C.-W., C.-F. Chien, and M. Gen (2014), “A Multiobjective Hybrid Genetic Algorithm for TFT-LCD Module Assembly Scheduling,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 3, pp. 692-705.
Colombo, A. W., R. Schoop, and R. Neubert (2006), “An agent-based intelligent control platform for industrial holonic manufacturing systems,” IEEE Transactions on Industrial Electronics, vol. 53, no. 1, pp. 322-337.
Dauzère-Pérès, S. and L. Mönch (2013), “Scheduling jobs on a single batch processing machine with incompatible job families and weighted number of tardy jobs objective,” Computers & Operations Research, vol. 40, no. 5, pp. 1224-1233.
Davenport, T. H. (2009), “Make better decisions,” Harvard Business Review, vol. 87, no. 11, pp. 117-123.
Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan (2002), “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197.
Deen, S. M. (2003), Agent-based manufacturing: advances in the holonic approach, Berlin: Springer-Verlag.
Dijkstra, E. W. (1959), “A note on two problems in connexion with graphs,” Numerische mathematik, vol. 1, no. 1, pp. 269-271.
ElMaraghy, H., V. Patel, and I. B. Abdallah (2000), “Scheduling of manufacturing systems under dual-resource constraints using genetic algorithms,” Journal of Manufacturing Systems, vol. 19, no. 3, pp. 186-201.
Ernst, A. T., H. Jiang, M. Krishnamoorthy, and D. Sier (2004), “Staff scheduling and rostering: A review of applications, methods and models,” European Journal of Operational Research, vol. 153, no. 1, pp. 3-27.
Farahani, R. Z. and M. Elahipanah (2008), “A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain,” International Journal of Production Economics, vol. 111, no. 2, pp. 229-243.
Fazlollahtabar, H. and M. Saidi-Mehrabad (2015), “Methodologies to optimize automated guided vehicle scheduling and routing problems: a review study,” Journal of Intelligent & Robotic Systems, vol. 77, no. 3, pp. 525-545.
Fletcher, M. and S. M. Deen (2001), “Fault-tolerant holonic manufacturing systems,” Concurrency and Computation: Practice and Experience, vol. 13, no. 1, pp. 43-70.
Francis, R. L., L. McGinnis, and J. A. White. (1992). Facility Layout and Location: An Analytical Approach: Prentice-Hall, Englewood Cliffs, NJ.
Gao, J., L. Sun, and M. Gen (2008), “A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems,” Computers & Operations Research, vol. 35, no. 9, pp. 2892-2907.
Gargeya, V. B. and R. H. Deane (1999), “Scheduling in the dynamic job shop under auxiliary resource constraints: A simulation study,” International Journal of Production Research, vol. 37, no. 12, pp. 2817-2834.
Gen, M., R. Cheng, and L. Lin (2008), Network models and optimization: multiple objective genetic algorithm approach, London: Springer.
Giannakis, M. and M. Louis (2011), “A multi-agent based framework for supply chain risk management,” Journal of Purchasing and Supply Management, vol. 17, no. 1, pp. 23-31.
Govind, N., T. M. Roeder, and L. W. Schruben (2011), “A Simulation-Based Closed Queueing Network Approximation of Semiconductor Automated Material Handling Systems,” IEEE Transactions on Semiconductor Manufacturing, vol. 24, no. 1, pp. 5-13.
Guo, Q. and M. Zhang (2009), “A novel approach for multi-agent-based Intelligent Manufacturing System,” Information Sciences, vol. 179, no. 18, pp. 3079-3090.
Gupta, A. K. and A. I. Sivakumar (2005), “Single machine scheduling with multiple objectives in semiconductor manufacturing,” The International Journal of Advanced Manufacturing Technology, vol. 26, no. 9-10, pp. 950-958.
Hao, Q., W. Shen, and L. Wang (2005), “Towards a cooperative distributed manufacturing management framework,” Computers in Industry, vol. 56, no. 1, pp. 71-84.
He, N., D. Z. Zhang, and Q. Li (2014), “Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system,” International Journal of Production Economics, vol. 149, no. 0, pp. 117-130.
Holdren, J. (2012). A National strategic plan for advanced manufacturing. Washington, DC: Executive Office of the President National Science and Technology Council.
Holdren, J., E. Lander, S. Jackson, and E. Schmidt. (2011). Report to the President on Ensuring American Leadership in Advanced Manufacturing. Washington, DC: Executive Office of the President President’s Council of Advisors on Science and Technology.
Hsieh, C.-H., C. Cho, T. Yang, and T.-J. Chang (2012), “Simulation study for a proposed segmented automated material handling system design for 300-mm semiconductor fabs,” Simulation Modelling Practice and Theory, vol. 29, pp. 18-31.
Huang, C.-J., K.-H. Chang, and J. T. Lin (2012), “Optimal vehicle allocation for an Automated Materials Handling System using simulation optimisation,” International Journal of Production Research, vol. 50, no. 20, pp. 5734-5746.
Hull, B., V. Bychkovsky, Y. Zhang, K. Chen, M. Goraczko, A. Miu, E. Shih, H. Balakrishnan, and S. Madden (2006) "CarTel: a distributed mobile sensor computing system," Proceeding of the 4th international conference on Embedded networked sensor systems.
Hung, P.-C. and F.-H. Liu (2001), “Estimation of the fleet size for a multi-load Automated Guided Vehicle System,” Proceedings of National Science Council: Physical Science and Engineering, vol. 25, no. 4, pp. 244-253.
Hwang, C. L. and K. Yoon (1981), Multiple attribute decision making: Methods and applications, Berlin: Springer-Verlage.
Jennings, N. R. (2001), “An agent-based approach for building complex software systems,” Communications of the ACM, vol. 44, no. 4, pp. 35-41.
Jennings, N. R. and M. Wooldridge. (1998), Applications of intelligent agents. In N. R. Jennings and M. Wooldridge (Eds.), Agent Technology: Foundations, Applications, and Markets, pp. 3-28, Berlin: Springer.
Jeong, B., S.-W. Kim, and Y.-J. Lee (2001), “An assembly scheduler for TFT LCD manufacturing,” Computers & Industrial Engineering, vol. 41, no. 1, pp. 37-58.
Kacem, I., S. Hammadi, and P. Borne (2002), “Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 32, no. 1, pp. 1-13.
Kagermann, H., J. Helbig, A. Hellinger, and W. Wahlster (2013a), Recommendations for Implementing the Strategic Initiative Industrie 4.0: Final Report of the Industrie 4.0 Working Group, Berlin, Germany: Federal Ministry of Education and Research.
Kagermann, H., J. Helbig, A. Hellinger, and W. Wahlster (2013b), Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry, German: Forschungsunion.
Kim, B.-I., J. Shin, and J. Chae (2009), “Simple blocking prevention for bay type path-based automated material handling systems,” The International Journal of Advanced Manufacturing Technology, vol. 44, no. 7-8, pp. 809-816.
Koestler, A. (1967), The ghost in the machine, London: Hutchinson.
Lau, H. Y. K. and S. O. Woo (2008), “An agent-based dynamic routing strategy for automated material handling systems,” International Journal of Computer Integrated Manufacturing, vol. 21, no. 3, pp. 269-288.
Le-Anh, T. and M. B. M. De Koster (2006), “A review of design and control of automated guided vehicle systems,” European Journal of Operational Research, vol. 171, no. 1, pp. 1-23.
Lee, A. H. I., S. H. Chung, and C. Y. Huang. (2007), Minimizing the Total Completion Time for the TFT-Array Factory Scheduling Problem (TAFSP). In O. Gervasi and M. Gavrilova (Eds.), Computational Science and Its Applications – ICCSA 2007, Vol. 4705, pp. 767-778, Kuala Lumpur: Springer Berlin Heidelberg.
Lee, C.-Y., C.-H. Chen, and C.-F. Chien (2014), “A simulation analysis for evaluating TFT-LCD fab capacity expansion with a distant transportation problem,” International Journal of Production Research, vol. 52, no. 6, pp. 1868-1885.
Lee, E. A. (2008) "Cyber Physical Systems: Design Challenges," Proceeding of the 11th IEEE International Symposium on Object Oriented Real-Time Distributed Computing (ISORC).
Lee, E. A. and S. A. Seshia (2011), Introduction to embedded systems: A cyber-physical systems approach, Berkeley: Lee & Seshia.
Lee, S.-C., T.-S. Hsu, S.-P. Chuang, and C.-L. Yang (2008), “Enhanced performance of overlap flow-shop scheduling involving reworking and a time buffer,” The International Journal of Advanced Manufacturing Technology, vol. 39, no. 1-2, pp. 142-150.
Lei, D. (2009), “Multi-objective production scheduling: a survey,” The International Journal of Advanced Manufacturing Technology, vol. 43, no. 9-10, pp. 926-938.
Lei, D. and X. Guo (2013), “Variable neighbourhood search for dual-resource constrained flexible job shop scheduling,” International Journal of Production Research, vol. 52, no. 9, pp. 2519-2529.
Leitão, P. (2010), “A holonic disturbance management architecture for flexible manufacturing systems,” International Journal of Production Research, vol. 49, no. 5, pp. 1269-1284.
Leitão, P. and F. Restivo (2008), “A holonic approach to dynamic manufacturing scheduling,” Robotics and Computer-Integrated Manufacturing, vol. 24, no. 5, pp. 625-634.
Leitao, P., V. Marik, and P. Vrba (2013), “Past, Present, and Future of Industrial Agent Applications,” IEEE Transactions on Industrial Informatics, vol. 9, no. 4, pp. 2360-2372.
Leung, C. W., T. N. Wong, K. L. Mak, and R. Y. K. Fung (2010), “Integrated process planning and scheduling by an agent-based ant colony optimization,” Computers & Industrial Engineering, vol. 59, no. 1, pp. 166-180.
Li, L., K. P. Logan, D. A. Cartes, and S. K. Srivastava (2007), “Fault Detection, Diagnostics, and Prognostics: Software Agent Solutions,” IEEE Transactions on Vehicular Technology, vol. 56, no. 4, pp. 1613-1622.
Lin, J. T., F.-K. Wang, and P.-Y. Yen (2001), “Simulation analysis of dispatching rules for an automated interbay material handling system in wafer fab,” International Journal of Production Research, vol. 39, no. 6, pp. 1221-1238.
Lin, J. T., F. K. Wang, and Y. M. Chang (2006), “A hybrid push/pull-dispatching rule for a photobay in a 300mm wafer fab,” Robotics and Computer-Integrated Manufacturing, vol. 22, no. 1, pp. 47-55.
Lin, L. and M. Gen (2009), “Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation,” Soft Computing, vol. 13, no. 2, pp. 157-168.
Lind, M. and O. Roulet-Dubonnet (2010), “Holonic shop-floor application for handling, feeding and transportation of workpieces,” International Journal of Production Research, vol. 49, no. 5, pp. 1441-1454.
Liu, H.-C. and Y. Yih (2013), “A fuzzy-based approach to the liquid crystal injection scheduling problem in a TFT-LCD fab,” International Journal of Production Research, vol. 51, no. 20, pp. 6163-6181.
Liu, M. and C. Wu (2004), “Genetic algorithm using sequence rule chain for multi-objective optimization in re-entrant micro-electronic production line,” Robotics and Computer-Integrated Manufacturing, vol. 20, no. 3, pp. 225-236.
Mönch, L., J. Fowler, S. Dauzère-Pérès, S. Mason, and O. Rose (2011), “A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations,” Journal of Scheduling, vol. 14, no. 6, pp. 583-599.
Mönch, L. and M. Stehli (2006), “ManufAg: a multi-agent-system framework for production control of complex manufacturing systems,” Information Systems and e-Business Management, vol. 4, no. 2, pp. 159-185.
MacGregor, J. and A. Cinar (2012), “Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods,” Computers & Chemical Engineering, vol. 47, no. 0, pp. 111-120.
Mackulak, G. T. and P. Savory (2001), “A simulation-based experiment for comparing AMHS performance in a semiconductor fabrication facility,” IEEE Transactions on Semiconductor Manufacturing, vol. 14, no. 3, pp. 273-280.
Mahdjoub, M., D. Monticolo, S. Gomes, and J.-C. Sagot (2010), “A collaborative Design for Usability approach supported by Virtual Reality and a Multi-Agent System embedded in a PLM environment,” Computer-Aided Design, vol. 42, no. 5, pp. 402-413.
Monostori, L., J. Váncza, and S. R. T. Kumara (2006), “Agent-Based Systems for Manufacturing,” CIRP Annals - Manufacturing Technology, vol. 55, no. 2, pp. 697-720.
Montoya-Torres, J. R. (2006), “A literature survey on the design approaches and operational issues of automated wafer-transport systems for wafer fabs,” Production Planning & Control, vol. 17, no. 7, pp. 648-663.
Nazzal, D. and L. F. McGinnis (2007), “Analytical approach to estimating AMHS performance in 300 mm fabs,” International Journal of Production Research, vol. 45, no. 3, pp. 571-590.
Otto, J., S. Henning, and O. Niggemann (2014), “Why Cyber-physical Production Systems Need a Descriptive Engineering Approach–A Case Study in Plug & Produce,” Procedia Technology, vol. 15, pp. 295-302.
Ounnar, F. and P. Pujo (2012), “Pull control for job shop: holonic manufacturing system approach using multicriteria decision-making,” Journal of Intelligent Manufacturing, vol. 23, no. 1, pp. 141-153.
Pearn, W. L., J. S. Hong, and Y. T. Tai (2012), “The burn-in test scheduling problem with batch dependent processing time and sequence dependent setup time,” International Journal of Production Research, vol. 51, no. 6, pp. 1694-1706.
Peters, B. A. and T. Yang (1997), “Integrated facility layout and material handling system design in semiconductor fabrication facilities,” IEEE Transactions on Semiconductor Manufacturing, vol. 10, no. 3, pp. 360-369.
Pezzella, F., G. Morganti, and G. Ciaschetti (2008), “A genetic algorithm for the Flexible Job-shop Scheduling Problem,” Computers & Operations Research, vol. 35, no. 10, pp. 3202-3212.
Qin, W., J. Zhang, and Y. Sun (2013), “Multiple-objective scheduling for interbay AMHS by using genetic-programming-based composite dispatching rules generator,” Computers in Industry, vol. 64, no. 6, pp. 694-707.
Rajotia, S., K. Shanker, and J. L. Batra (1998), “Determination of optimal AGV fleet size for an FMS,” International Journal of Production Research, vol. 36, no. 5, pp. 1177-1198.
Reichelt, D. and L. Mönch. (2006), Multiobjective Scheduling of Jobs with Incompatible Families on Parallel Batch Machines. In J. Gottlieb and G. Raidl (Eds.), Evolutionary Computation in Combinatorial Optimization, Vol. 3906, pp. 209-221, Budapest: Springer Berlin Heidelberg.
Ruiz, R. and J. A. Vázquez-Rodríguez (2010), “The hybrid flow shop scheduling problem,” European Journal of Operational Research, vol. 205, no. 1, pp. 1-18.
Russell, S. and P. Norvig (1995), Artificial Intelligence: A Modern Approach, Englewood Cliffs: Prentice Hall.
Süer, G. A., J. Cosner, and A. Patten (2009), “Models for cell loading and product sequencing in labor-intensive cells,” Computers & Industrial Engineering, vol. 56, no. 1, pp. 97-105.
Sarin, S. C., A. Varadarajan, and L. Wang (2011), “A survey of dispatching rules for operational control in wafer fabrication,” Production Planning & Control, vol. 22, no. 1, pp. 4-24.
SEMATECH. (2003). e-Diagnostics and EEC Guidance 2003. Austin, Texas: SEMATECH.
Seo, D. K., C. M. Klein, and W. Jang (2005), “Single machine stochastic scheduling to minimize the expected number of tardy jobs using mathematical programming models,” Computers & Industrial Engineering, vol. 48, no. 2, pp. 153-161.
Shen, W., Q. Hao, S. Wang, Y. Li, and H. Ghenniwa (2007), “An agent-based service-oriented integration architecture for collaborative intelligent manufacturing,” Robotics and Computer-Integrated Manufacturing, vol. 23, no. 3, pp. 315-325.
Shen, W., Q. Hao, H. J. Yoon, and D. H. Norrie (2006), “Applications of agent-based systems in intelligent manufacturing: An updated review,” Advanced Engineering Informatics, vol. 20, no. 4, pp. 415-431.
Shin, H. J. and V. J. Leon (2004), “Scheduling with product family set-up times: an application in TFT LCD manufacturing,” International Journal of Production Research, vol. 42, no. 20, pp. 4235-4248.
Srivastava, S., A. Choudhary, S. Kumar, and M. K. Tiwari (2008), “Development of an intelligent agent-based AGV controller for a flexible manufacturing system,” The International Journal of Advanced Manufacturing Technology, vol. 36, no. 7-8, pp. 780-797.
Tharumarajah, A. (2003), From Fractals and Bionics to Holonics. In S. M. Deen (Ed.), Agent-based manufacturing: advances in the holonic approach, pp. 11-30, Berlin: Springer-Verlag.
Ting, J.-H. and J. M. A. Tanchoco (2000), “Unidirectional circular layout for overhead material handling systems,” International Journal of Production Research, vol. 38, no. 16, pp. 3913-3935.
Ting, J.-H. and J. M. A. Tanchoco (2001), “Optimal bidirectional spine layout for overhead material handling systems,” IEEE Transactions on Semiconductor Manufacturing, vol. 14, no. 1, pp. 57-64.
Tiwari, R. N., S. Dharmar, and J. R. Rao (1987), “Fuzzy goal programming — An additive model,” Fuzzy Sets and Systems, vol. 24, no. 1, pp. 27-34.
Trappey, A. J. C., C. V. Trappey, J. L. Hou, and B. J. G. Chen (2004), “Mobile agent technology and application for online global logistic services,” Industrial Management & Data Systems, vol. 104, no. 2, pp. 169-183.
Tseng, F.-M., Y.-J. Chiu, and J.-S. Chen (2009), “Measuring business performance in the high-tech manufacturing industry: A case study of Taiwan's large-sized TFT-LCD panel companies,” Omega, vol. 37, no. 3, pp. 686-697.
Tu, Y.-M., C.-W. Lu, and A. H. I. Lee (2013), “AMHS capacity determination model for wafer fabrication based on production performance optimization,” International Journal of Production Research, vol. 51, no. 18, pp. 5520-5535.
Ulieru, M. and D. Norrie (2000), “Fault recovery in distributed manufacturing systems by emergent holonic re-configuration: A fuzzy multi-agent modeling approach,” Information Sciences, vol. 127, no. 3–4, pp. 101-123.
Van Brussel, H., J. Wyns, P. Valckenaers, L. Bongaerts, and P. Peeters (1998), “Reference architecture for holonic manufacturing systems: PROSA,” Computers in Industry, vol. 37, no. 3, pp. 255-274.
Van den Bergh, J., J. Beliën, P. De Bruecker, E. Demeulemeester, and L. De Boeck (2013), “Personnel scheduling: A literature review,” European Journal of Operational Research, vol. 226, no. 3, pp. 367-385.
Vis, I. F. A. (2006), “Survey of research in the design and control of automated guided vehicle systems,” European Journal of Operational Research, vol. 170, no. 3, pp. 677-709.
Wang, H.-K., C.-F. Chien, and M. Gen (2015), “An Algorithm of Multi-Subpopulation Parameters With Hybrid Estimation of Distribution for Semiconductor Scheduling With Constrained Waiting Time,” IEEE Transactions on Semiconductor Manufacturing, vol. 28, no. 3, pp. 353-366.
Wang, J. X., M. X. Tang, L. N. Song, and S. Q. Jiang (2009a), “Design and implementation of an agent-based collaborative product design system,” Computers in Industry, vol. 60, no. 7, pp. 520-535.
Wang, K.-J. and C.-H. Chou (2001), “Modeling of agent-based manufacturing systems,” Journal of the Chinese Institute of Industrial Engineers, vol. 18, no. 1, pp. 1-14.
Wang, K.-J., Y. S. Lin, C.-F. Chien, and J. C. Chen (2009b), “A fuzzy-knowledge resource-allocation model of the semiconductor final test industry,” Robotics and Computer-Integrated Manufacturing, vol. 25, no. 1, pp. 32-41.
Wang, L.-C. and S.-K. Lin (2009), “A multi-agent based agile manufacturing planning and control system,” Computers & Industrial Engineering, vol. 57, no. 2, pp. 620-640.
Wang, M., H. Wang, and D. Xu (2005), “The design of intelligent workflow monitoring with agent technology,” Knowledge-Based Systems, vol. 18, no. 6, pp. 257-266.
Wang, M. J. J., H.-C. Chung, and H.-C. Wu (2003), “The evaluation of manual FOUP handling in 300-mm wafer fab,” IEEE Transactions on Semiconductor Manufacturing, vol. 16, no. 3, pp. 551-554.
Wright, P. K. and D. A. Bourne (1988), Manufacturing intelligence, Reading, MA: Addison-Wesley.
Wu, J.-Z. and C.-F. Chien (2008), “Modeling semiconductor testing job scheduling and dynamic testing machine configuration,” Expert Systems with Applications, vol. 35, no. 1–2, pp. 485-496.
Wu, J.-Z., X.-C. Hao, C.-F. Chien, and M. Gen (2012), “A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem,” Journal of Intelligent Manufacturing, vol. 23, no. 6, pp. 2255-2270.
Xu, J., X. Xu, and S. Q. Xie (2011), “Recent developments in Dual Resource Constrained (DRC) system research,” European Journal of Operational Research, vol. 215, no. 2, pp. 309-318.
Yoshikawa, H. (1993) "Intelligent Manufacturing Systems: Technical Cooperation that Transcends Cultural Differences," Proceeding of the DIISM '93: Proceedings of the JSPE/IFIP TC5/WG5.3 Workshop on the Design of Information Infrastructure Systems for Manufacturing.
Yun, Y. and M. Gen (2003), “Performance Analysis of Adaptive Genetic Algorithms with Fuzzy Logic and Heuristics,” Fuzzy Optimization and Decision Making, vol. 2, no. 2, pp. 161-175.
Zhao, F., Y. Hong, D. Yu, Y. Yang, and Q. Zhang (2010), “A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems,” International Journal of Computer Integrated Manufacturing, vol. 23, no. 1, pp. 20-39.
Zimmermann, H. J. (1978), “Fuzzy programming and linear programming with several objective functions,” Fuzzy Sets and Systems, vol. 1, no. 1, pp. 45-55.
Zuehlke, D. (2010), “SmartFactory—Towards a factory-of-things,” Annual Reviews in Control, vol. 34, no. 1, pp. 129-138.