自德國提出工業4.0提出後，智能製造已成為全世界重要的議題。諸如美國、中國皆提出先進製造夥伴2.0、中國製造2025等國家型計畫以因應智慧製造之發展。然而，台灣及其他新興國家的產業結構與德國或美國等領先國家不同，產業的升級與相關政策的推動亦需要長期累積，智慧製造的發展並未能一步到位，因此如何結合工業3.0自動化之基礎、製造管理的經驗、以及生產製造現場調度能力發展智慧製造，並逐步往工業4.0邁進，是對於台灣等新興國家關鍵的問題。
本研究提出基於工業3.5的智慧製造系統架構，旨在提出可於目前製造環境下發展智慧製造之藍圖。本架構整合製造端資訊，藉以串連製造子系統、資料庫以及相關支援技術。根據本研究提出之架構，產線可逐步由自動化邁向彈性決策支援，並最終升級為工業4.0之智慧工廠。
本研究以薄膜液晶顯示器之陣列製造動態排程為實證研究。作者提出以模擬為基準的動態排程與派工模型求解陣列製造排程。研究結果顯示，本研究提出之混合基因演算法可有效減少瓶頸站點之產能損失，同時可降低因工件延誤所造成的缺陷及報廢。此外，陣列廠之生產績效如延誤工件數量及總延誤時間，亦能藉由於非瓶頸站點導入組合派工法則而有效改善。本研究發展之模型亦能導入實際產線中，並嵌入其決策支援系統。

Smart manufacturing has become a crucial issue since the propose of Industry 4.0 by Germany. Similar proposals, including the Advanced Manufacturing Partnership 2.0 (AMP2.0) by the United States and Made in China 2025, are also national plans to achieve the smart manufacturing. There are many developed or developing countries also try to step into the field of smart manufacturing. However, most of the emerging countries may not be ready for the migration of Industry 4.0 directly since their industrial infrastructures are different from that of the leading countries. It is crucial to have a transitional plan to make up the gap and maintain competiveness in the transitional stage between Industry 3.0 and to-be Industry 4.0. This study proposes a framework of smart manufacturing system based on Industry 3.5, which aims to achieve smart manufacturing under the existing environments. The framework is constructed by three subsystems, database, supporting technologies, as well as the integration of information. Based on the proposed framework, it provides a blueprint from the existing manufacturing systems towards the smart factory.
An empirical study was conducted at a leading TFT-LCD company. This study proposed a simulation-based dynamic scheduling and dispatching model for TFT-LCD array manufacturing. Results show that the capacity loss of bottleneck workstation and delivery tardiness can be significantly improved by the proposed hybrid genetic algorithm (HGA). Besides, the overall array performances, such as mean value of tardy jobs and total tardiness, can be improved by employing combinatorial dispatching rules at non-bottleneck workstations. Finally, this model can be embedded in the practical production lines to support the decisions of practical manufacturing systems.

Table of Contents vi
List of Figures ix
List of Tables xii
List of Abbreviations xiv
Nomenclature xv
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Research aims 5
1.4 Dissertation organization 6
Chapter 2 Literature review 7
2.1 Manufacturing strategies 7
2.1.1 Evolution of manufacturing system 7
2.1.2 Industry 4.0 9
2.1.3 Industry 3.5 13
2.1.4 Smart factory 15
2.2 Scheduling problems 18
2.2.1 Classification of scheduling problem 18
2.2.2 TFT-LCD scheduling problem 21
2.3 Methodologies for scheduling problem 23
2.3.1 Dispatching rules 23
2.3.2 Mathematical models 24
2.3.3 Meta-heuristics 25
2.3.4 Discrete-event simulation 27
Chapter 3 The framework of smart manufacturing system for Industry 3.5
29
3.1 Research framework 29
3.2 Simulation-based smart manufacturing system 32
3.2.2 Production planning system 34
3.2.3 Dynamic scheduling and dispatching system 36
3.2.4 Control and monitoring system 39
Chapter 4 TFT-LCD array photolithography stages scheduling problem 41
4.1 Problem structuring 41
4.1.1 Limited waiting time constraints 42
4.1.2 Scheduling period 42
4.1.3 Photo mask availability 42
4.1.4 Photo mask transportation time 43
4.1.5 Available machines with different processing for jobs 43
4.1.6 Different arrival time of jobs 43
4.2 Problem formulation 44
4.3 Simulation-based photo stages scheduling model 49
4.3.1. Chromosome representation and initialization 52
4.3.2. Decoding and evaluation 53
4.3.3. Crossover, mutation and selection 55
4.3.4. Local search 57
4.3.5. Randomized local search 57
4.4 Experimental design 58
4.4.1 Small cases 58
4.4.2 Practical experiments 59
4.5 Validation 61
4.5.2 Result of small cases 62
4.5.3 Result of practical experiments 63
4.6 Discussion 69
Chapter 5 TFT-LCD array photolithography stages scheduling problem 72
5.1 Problem background 72
5.2 Dispatching decision for simulation-based DSDS 74
5.2.2 Candidate dispatching rules 76
5.2.3 Min-max normalization and performance evaluation 78
5.2.4 TOPSIS 79
5.3 Empirical experiments 81
5.3.1 Identifying critical workstations and reducing candidate rules
81
5.3.2 Results of experiments 86
5.4 Discussion 91
Chapter 6 Conclusions 94
References 98

Abdullah, S. and Abdolrazzagh-Nezhad, M. (2014), “Fuzzy job-shop scheduling problems: A review,” Information Sciences, Vol. 278, No., pp. 380-407.
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., and Ayyash, M. (2015), “Internet of things: A survey on enabling technologies, protocols, and applications,” IEEE Communications Surveys & Tutorials, Vol. 17, No. 4, pp. 2347-2376.
Amjad, M. K., Butt, S. I., Kousar, R., Ahmad, R., Agha, M. H., Faping, Z., Anjum, N., and Asgher, U. (2018), “Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems,” Mathematical Problems in Engineering.
Bahl, H. C., Ritzman, L. P., and Gupta, J. N. (1987), “OR practice—Determining lot sizes and resource requirements: A review,” Operations Research, Vol. 35, No. 3, pp. 329-345.
Bi, Z. M., Lang, S. Y. T., Shen, W., and Wang, L. (2008), “Reconfigurable manufacturing systems: the state of the art,” International Journal of Production Research, Vol. 46, No. 4, pp. 967-992.
Bi, Z., Xu, L. D., and Wang, C. (2014), “Internet of things for enterprise systems of modern manufacturing,” IEEE Transactions on industrial informatics, Vol. 10, No. 2, pp. 1537-1546.
Biswas, S. and Narahari, Y. (2004), “Object oriented modeling and decision support for supply chains,” European Journal of Operational Research, Vol. 153, No. 3, pp. 704-726.
Bitar, A., Dauzère-Pérès, S., Yugma, C., and Roussel, R. (2016), “A memetic algorithm to solve an unrelated parallel machine scheduling problem with auxiliary resources in semiconductor manufacturing,” Journal of Scheduling, Vol. 19, No. 4, pp. 367-376.
Blazewicz, J., Dror, M., and Weglarz, J. (1991), “Mathematical programming formulations for machine scheduling: A survey,” European Journal of Operational Research, Vol. 51, No. 3, pp. 283-300.
Brettel, M., Friederichsen, N., Keller, M., and Rosenberg, M. (2014), “How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective,” International Journal of Mechanical, Industrial Science and Engineering, Vol. 8, No. 1, pp. 37-44.
Cakici, E. and Mason, S. J. (2007), “Parallel machine scheduling subject to auxiliary resource constraints,” Production Planning & Control, Vol. 18, No. 3, pp. 217-225.
Chan, F., Chan, H., and Lau, H. (2002), “The state of the art in simulation study on FMS scheduling: a comprehensive survey,” The International Journal of Advanced Manufacturing Technology, Vol. 19, No. 11, pp. 830-849.
Chaudhry, I. A. and Khan, A. A. (2016), “A research survey: review of flexible job shop scheduling techniques,” International Transactions in Operational Research, Vol. 23, No. 3, pp. 551-591.
Chen, B. and Matis, T. I. (2013), “A flexible dispatching rule for minimizing tardiness in job shop scheduling,” International Journal of Production Economics, Vol. 141, No. 1, pp. 360-365.
Chen, J. C., Chen, T.-L., Pratama, B. R., and Tu, Q.-F. (2018), “Capacity planning with ant colony optimization for TFT-LCD array manufacturing,” Journal of Intelligent Manufacturing, Vol. 29, No. 8, pp. 1695-1713.
Chen, J. C., Huang, P. B., Wu, J.-G., Lai, K. Y., Chen, C.-C., and Peng, T.-W. (2013), “Advanced planning and scheduling for TFT-LCD color filter fab with multiple lines,” The International Journal of Advanced Manufacturing Technology, Vol. 67, No. 1, pp. 101-110.
Chen, Y.-Y. and Lin, J. T. (2009), “A modified particle swarm optimization for production planning problems in the TFT Array process,” Expert Systems with Applications, Vol. 36, No. 10, pp. 12264-12271.
Chien, C.-F., Chen, H.-K., Wu, J.-Z., and Hu, C.-H. (2007), “Constructing the OGE for promoting tool group productivity in semiconductor manufacturing,” International Journal of Production Research, Vol. 45, No. 3, pp. 509-524.
Chien, C.-F., Chen, Y.-J., and Hsu, C.-Y. (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, No., pp. 309-318.
Chien, C.-F., Chou, C.-W., and Yu, H.-C. (2016), “A novel route selection and resource allocation approach 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, Vol. 13, No. 4, pp. 1567-1580.
Chien, C.-F., Hong, T.-Y., and Guo, H.-Z. (2017), “An empirical study for smart production for TFT-LCD to empower Industry 3.5,” Journal of the Chinese Institute of Engineers, Vol. 40, No. 7, pp. 552-561.
Chien, C.-F., Hsu, C.-Y., and Chang, K.-H. (2013a), “Overall wafer effectiveness (OWE): A novel industry standard for semiconductor ecosystem as a whole,” Computers & Industrial Engineering, Vol. 65, No. 1, pp. 117-127.
Chien, C.-F., Hsu, C.-Y., and Chen, P.-N. (2013b), “Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence,” Flexible Services and Manufacturing Journal, Vol. 25, No. 3, pp. 367-388.
Chien, C.-F., Lin, K.-Y., Sheu, J.-B., and Wu, C.-H. (2016), “Retrospect and Prospect on Operations and Management Journals in Taiwan: From Industry 3.0 to Industry 3.5,” Journal of Management and Business Research, Vol. 33, No. 1, pp. 87-103. (In Chinese)
Cho, H.-M. and Jeong, I.-J. (2017), “A two-level method of production planning and scheduling for bi-objective reentrant hybrid flow shops,” Computers & Industrial Engineering, Vol. 106, No., pp. 174-181.
Choi, H.-S., Kim, J.-S., and Lee, D.-H. (2011), “Real-time scheduling for reentrant hybrid flow shops: A decision tree based mechanism and its application to a TFT-LCD line,” Expert systems with Applications, Vol. 38, No. 4, pp. 3514-3521.
Chou, C.-W., Chien, C.-F., and Gen, M. (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.
Chung, S., Pearn, W. L., and Tai, Y. (2009), “Fast and effective algorithms for the liquid crystal display module (LCM) scheduling problem with sequence-dependent setup time,” Journal of the Operational Research Society, Vol. 60, No. 7, pp. 921-933.
Consortium, I. I. (2017). Smart factory applications in discrete manufacturing. https://www.iiconsortium.org/pdf/Smart_Factory_Applications_in_Discrete_Mfg_white_paper_20170222.pdf.
Cristobal, M. P., Escudero, L. F., and Monge, J. F. (2009), “On stochastic dynamic programming for solving large-scale planning problems under uncertainty,” Computers & Operations Research, Vol. 36, No. 8, pp. 2418-2428.
Demir, Y. and İşleyen, S. K. (2013), “Evaluation of mathematical models for flexible job-shop scheduling problems,” Applied Mathematical Modelling, Vol. 37, No. 3, pp. 977-988.
Dhiman, G. and Kumar, V. (2017), “Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications,” Advances in Engineering Software, Vol. 114, No., pp. 48-70.
Díaz-Madroñero, M., Mula, J., and Peidro, D. (2014), “A review of discrete-time optimization models for tactical production planning,” International Journal of Production Research, Vol. 52, No. 17, pp. 5171-5205.
Dorigo, M. and Di Caro, G. (1999), “Ant colony optimization: a new meta-heuristic,” Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), July 6-9, Washington DC, USA.
Drath, R. and Horch, A. (2014), “Industrie 4.0: Hit or Hype? [Industry Forum],” IEEE Industrial Electronics Magazine, Vol. 8, No. 2, pp. 56-58.
Edis, E. B., Oguz, C., and Ozkarahan, I. (2013), “Parallel machine scheduling with additional resources: Notation, classification, models and solution methods,” European Journal of Operational Research, Vol. 230, No. 3, pp. 449-463.
El-Bouri, A. (2012), “A cooperative dispatching approach for minimizing mean tardiness in a dynamic flowshop,” Computers & Operations Research, Vol. 39, No. 7, pp. 1305-1314.
ElMaraghy, H. A. (2005), “Flexible and reconfigurable manufacturing systems paradigms,” International journal of flexible manufacturing systems, Vol. 17, No. 4, pp. 261-276.
Executive Office of the President of the United States (2014), “Accelerating U.S. Advanced Manufacturing, AMP2.0 Steering Committee Report,” The President’s Council of Advisors on Science and Technology of the United States.
Executive Yuan of Taiwan (2015), “Executive Yuan of Taiwan announced the launch of the Productivity 4.0 Project,” available: https://www.nchu.edu.tw/~class/bulletin/MOE/105_MoE_re_allr.pdf.
Federal Ministry of Education and Research (2013), “Recommendation the Strategic Initiative INDUSTRIE 4.0,” National Academy of Science and Engineering of Germany.
Framinan, J. M., Gupta, J. N., and Leisten, R. (2004), “A review and classification of heuristics for permutation flow-shop scheduling with makespan objective,” Journal of the Operational Research Society, Vol. 55, No. 12, pp. 1243-1255.
Geem, Z. W., Kim, J. H., and Loganathan, G. V. (2001), “A new heuristic optimization algorithm: harmony search,” Simulation, Vol. 76, No. 2, pp. 60-68.
Gonzalez-R, P. L., Framinan, J. M., and Ruiz-Usano, R. (2010), “A multi-objective comparison of dispatching rules in a drum–buffer–rope production control system,” International Journal of Computer Integrated Manufacturing, Vol. 23, No. 2, pp. 155-167.
Gubbi, J., Buyya, R., Marusic, S., and Palaniswami, M. (2013), “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future generation computer systems, Vol. 29, No. 7, pp. 1645-1660.
Gunasekaran, A., Yusuf, Y. Y., Adeleye, E. O., Papadopoulos, T., Kovvuri, D., and Geyi, D. A. G. (2018), “Agile manufacturing: an evolutionary review of practices,” International Journal of Production Research, pp. 1-21.
Guo, C., Zhibin, J., Zhang, H., and Li, N. (2012), “Decomposition-based classified ant colony optimization algorithm for scheduling semiconductor wafer fabrication system,” Computers & Industrial Engineering, Vol. 62, No. 1, pp. 141-151.
Ham, A. (2018), “Scheduling of Dual Resource Constrained Lithography Production: Using CP and MIP/CP,” IEEE Transactions on Semiconductor Manufacturing, Vol. 31, No. 1, pp. 52-61.
Hao, J.-h., Liu, M., Lin, J.-h., and Wu, C. (2016), “A hybrid differential evolution approach based on surrogate modelling for scheduling bottleneck stages,” Computers & Operations Research, Vol. 66, No., pp. 215-224.
Hekmatfar, M., Ghomi, S. F., and Karimi, B. (2011), “Two stage reentrant hybrid flow shop with setup times and the criterion of minimizing makespan,” Applied Soft Computing, Vol. 11, No. 8, pp. 4530-4539.
Hermann, M., Pentek, T., and Otto, B. (2016), “Design principles for industrie 4.0 scenarios,” Proceedings of 49th Hawaii International Conference on System Sciences (HICSS), Jan 5-8, Koloa, HI, USA.
Hofmeyr, S. A. and Forrest, S. (2000), “Architecture for an artificial immune system,” Evolutionary computation, Vol. 8, No. 4, pp. 443-473.
Holland, J. H. (1992), “Genetic algorithms,” Scientific American, Vol. 267, No. 1, pp. 66-73.
Hong, T.-Y., Chien, C.-F., Wang, H.-K., and Guo, H.-Z. (2018), “A two-phase decoding genetic algorithm for TFT-LCD array photolithography stage scheduling problem with constrained waiting time,” Computers & Industrial Engineering, Vol. 125, No., pp. 200-211.
Hu, Z. and Hu, G. (2016), “A two-stage stochastic programming model for lot-sizing and scheduling under uncertainty,” International Journal of Production Economics, Vol. 180, No., pp. 198-207.
Huynh, N.-T. and Chien, C.-F. (2018), “A hybrid multi-subpopulation genetic algorithm for textile batch dyeing scheduling and an empirical study,” Computers & Industrial Engineering, Vol., No., pp.
Jamrus, T., Chien, C.-F., Gen, M., and Sethanan, K. (2018), “Hybrid Particle Swarm Optimization Combined With Genetic Operators for Flexible Job-Shop Scheduling Under Uncertain Processing Time for Semiconductor Manufacturing,” IEEE Transactions on Semiconductor Manufacturing, Vol. 31, No. 1, pp. 32-41.
Jazdi, N. (2014), “Cyber physical systems in the context of Industry 4.0,” Proceedings of IEEE International Conference on Automation, Quality and Testing, Robotics, May 22-24, Cluj-Napoca, Romania
Kanyalkar, A. and Adil, G. (2007), “Aggregate and detailed production planning integrating procurement and distribution plans in a multi-site environment,” International Journal of Production Research, Vol. 45, No. 22, pp. 5329-5353.
Kennedy, J. and Eberhart, R. (1995), “Particle swarm optimization,” Proceedings of ICNN'95 - International Conference on Neural Networks, Nov 27- Dec 21, Perth, WA, Australia.
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983), “Optimization by simulated annealing,” Science, Vol. 220, No. 4598, pp. 671-680.
Korytkowski, P., Wiśniewski, T., and Rymaszewski, S. (2013), “An evolutionary simulation-based optimization approach for dispatching scheduling,” Simulation Modelling Practice and Theory, Vol. 35, No., pp. 69-85.
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, Vol. 78, No. 1, pp. 40-56.
Lawrence, S. R. and Buss, A. H. (1994), “Shifting production bottlenecks: causes, cures, and conundrums,” Production and operations management, Vol. 3, No. 1, pp. 21-37.
Lee, A., Chung, S., and Huang, C. (2007), “Minimizing the total completion time for the TFT-array factory scheduling problem (TAFSP),” Proceedings of International Conference on Computational Science and Its Applications. pp. 767-778.
Lee, E. A. (2008), “Cyber physical systems: Design challenges,” Proceedings of 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing (ISORC), pp. 363-369
Lee, J., Bagheri, B., and Kao, H.-A. (2015), “A cyber-physical systems architecture for industry 4.0-based manufacturing systems,” Manufacturing Letters, Vol. 3, No., pp. 18-23.
Lin, J. T. and Chen, C.-M. (2015), “Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing,” Simulation Modelling Practice and Theory, Vol. 51, No., pp. 100-114.
Liu, H.-C. and Yih, Y. (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.
Low, C. and Yeh, Y. (2009), “Genetic algorithm-based heuristics for an open shop scheduling problem with setup, processing, and removal times separated,” Robotics and Computer-Integrated Manufacturing, Vol. 25, No. 2, pp. 314-322.
Lucke, D., Constantinescu, C., and Westkämper, E. (2008), “Smart factory-a step towards the next generation of manufacturing,” Manufacturing systems and technologies for the new frontier, Springer, pp. 115-118.
Mejía, G., Caballero-Villalobos, J. P., and Montoya, C. (2018), “Petri nets and deadlock-free scheduling of open shop manufacturing systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 48, No. 6, pp. 1017-1028.
Mokotoff, E. (2001), “Parallel machine scheduling problems: A survey,” Asia-Pacific Journal of Operational Research, Vol. 18, No. 2, pp. 193.
Mönch, L., Fowler, J. W., Dauzere-Peres, S., Mason, S. J., and Rose, O. (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.
Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., and Ueda, K. (2016), “Cyber-physical systems in manufacturing,” CIRP Annals, Vol. 65, No. 2, pp. 621-641.
Negahban, A. and Smith, J. S. (2014), “Simulation for manufacturing system design and operation: Literature review and analysis,” Journal of Manufacturing Systems, Vol. 33, No. 2, pp. 241-261.
Neufeld, J. S., Gupta, J. N., and Buscher, U. (2016), “A comprehensive review of flowshop group scheduling literature,” Computers & Operations Research, Vol. 70, No., pp. 56-74.
Ouelhadj, D. and Petrovic, S. (2008), “A survey of dynamic scheduling in manufacturing systems,” Journal of Scheduling, Vol. 12, No. 4, pp. 417.
Özgüven, C., Özbakır, L., and Yavuz, Y. (2010), “Mathematical models for job-shop scheduling problems with routing and process plan flexibility,” Applied Mathematical Modelling, Vol. 34, No. 6, pp. 1539-1548.
Passino, K. M. (2002), “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE control systems, Vol. 22, No. 3, pp. 52-67.
Pickardt, C. W., Hildebrandt, T., Branke, J., Heger, J., and Scholz-Reiter, B. (2013), “Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems,” International Journal of Production Economics, Vol. 145, No. 1, pp. 67-77.
Prajogo, D. and Olhager, J. (2012), “Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration,” International Journal of Production Economics, Vol. 135, No. 1, pp. 514-522.
Rajendran, C. and Holthaus, O. (1999), “A comparative study of dispatching rules in dynamic flowshops and jobshops,” European journal of operational research, Vol. 116, No. 1, pp. 156-170.
Ramasesh, R. (1990), “Dynamic job shop scheduling: a survey of simulation research,” Omega, Vol. 18, No. 1, pp. 43-57.
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, Vol. 37, No. 8, pp. 1439-1454.
Rose, O. (2001), “The shortest processing time first (SPTF) dispatch rule and some variants in semiconductor manufacturing,” Proceedings of Proceeding of the 2001 Winter Simulation Conference, Dec 9-12, Arlington, USA.
Rossit, D. A., Tohmé, F., and Frutos, M. (2018), “The non-permutation flow-shop scheduling problem: a literature review,” Omega, Vol. 77, No., pp. 143-153.
Ruiz, R. and Vázquez-Rodríguez, J. A. (2010), “The hybrid flow shop scheduling problem,” European journal of operational research, Vol. 205, No. 1, pp. 1-18.
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group.
SEMATECH (2003). E-Diagnostics and EEC Guidance 2003. SEMATECH, Austin, Texas.
Shewhart, W. A. (1931), Economic control of quality of manufactured product. ASQ Quality Press.
Shin, H. J. and Kang, Y. H. (2010), “A rework-based dispatching algorithm for module process in TFT-LCD manufacture,” International Journal of Production Research, Vol. 48, No. 3, pp. 915-931.
Shin, H. J., and Leon, V. J. (2004), “Scheduling with product family set-up times: an application in TFT LCD manufacturing,” International journal of production research, Vol. 42 No. 20, 4235-4248.
State Council of China (2015), “Made in China 2025,” available: http://fgw.chuzhou.gov.cn/download/58d37c0fe4b07e877d1ee5b3.
Storn, R. and Price, K. (1997), “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,” Journal of global optimization, Vol. 11, No. 4, pp. 341-359.
Tao, F., Cheng, Y., Da Xu, L., Zhang, L., and Li, B. H. (2014), “CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system,” IEEE Transactions on Industrial Informatics, Vol. 10, No. 2, pp. 1435-1442.
Tsai, C.-H., Feng, Y.-M., and Li, R.-K. (2003), “A hybrid dispatching rules in wafer fabrication factories,” International journal of the computer, the internet and management, Vol. 11, No. 1, pp. 64-72.
Tsai, C.-W. and Rodrigues, J. J. (2014), “Metaheuristic scheduling for cloud: A survey,” IEEE Systems Journal, Vol. 8, No. 1, pp. 279-291.
Vallada, E., Ruiz, R., and Minella, G. (2008), “Minimising total tardiness in the m-machine flowshop problem: A review and evaluation of heuristics and metaheuristics,” Computers & Operations Research, Vol. 35, No. 4, pp. 1350-1373.
Wang, H.-K., Chien, C.-F., and Chou, C.-W. (2017), “An empirical study of bio manufacturing for the scheduling of hepatitis in vitro diagnostic device with constrained process time window,” Computers & Industrial Engineering, Vol. 114, pp. 31-44.
Wang, H.-K., Chien, C.-F., and Gen, M. (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, I.-L., Yang, T., and Chang, Y.-B. (2012), “Scheduling two-stage hybrid flow shops with parallel batch, release time, and machine eligibility constraints,” Journal of Intelligent Manufacturing, Vol. 23, No. 6, pp. 2271-2280.
Wu, H.-H., Chen, C.-P., Tsai, C.-H., and Yang, C.-J. (2010), “Simulation and scheduling implementation study of TFT-LCD Cell plants using Drum–Buffer–Rope system,” Expert Systems with Applications, Vol. 37, No. 12, pp. 8127-8133.
Xiong, H., Fan, H., Jiang, G., and Li, G. (2017), “A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints,” European Journal of Operational Research, Vol. 257, No. 1, pp. 13-24.
Xu, L. D., He, W., and Li, S. (2014), “Internet of Things in Industries: A Survey,” IEEE Transactions on Industrial Informatics, Vol. 10, No. 4, pp. 2233-2243.
Xu, L. D., Xu, E. L., and Li, L. (2018), “Industry 4.0: state of the art and future trends,” International Journal of Production Research, Vol. 56, No. 8, pp. 2941-2962.
Yang, X.-S. (2010), “A new metaheuristic bat-inspired algorithm,” Nature inspired cooperative strategies for optimization (NICSO 2010), Springer, pp. 65-74.
Yang, X.-S. and Deb, S. (2009), “Cuckoo search via Lévy flights,” 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), Dec 9-11, Coimbatore, India.
Yeniay, Ö. (2005), “Penalty function methods for constrained optimization with genetic algorithms,” Mathematical and computational Applications, Vol. 10, No. 1, pp. 45-56.
Yoon, J.-S., Shin, S.-J., and Suh, S.-H. (2012), “A conceptual framework for the ubiquitous factory,” International Journal of Production Research, Vol. 50, No. 8, pp. 2174-2189.
Yuan, Y. and Xu, H. (2015), “Multiobjective flexible job shop scheduling using memetic algorithms,” IEEE Transactions on Automation Science and Engineering, Vol. 12, No. 1, pp. 336-353.
Zhang, J., Li, Z., and Wang, Z. (2010), “A multivariate control chart for simultaneously monitoring process mean and variability,” Computational Statistics & Data Analysis, Vol. 54, No. 10, pp. 2244-2252.
Zhang, P., Zhao, X., Sheng, X., and Zhang, J. (2018), “An Imperialist Competitive Algorithm Incorporating Remaining Cycle Time Prediction for Photolithography Machines Scheduling,” IEEE Access, Vol. 6, 66787-66797.
Zheng, P., Sang, Z., Zhong, R. Y., Liu, Y., Liu, C., Mubarok, K., Yu, S., and Xu, X. (2018), “Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives,” Frontiers of Mechanical Engineering, pp. 1-14.