決策無處不在，從服務到製造，從組織的高層到基層，第四次工業革命促使企業發展聰明製造技術，聰明營運管理和先進分析，其中聰明生產管理決策模型扮演著關鍵的角色。決策方法因問題而異，鑑別特定問題的有效性和有效率的方法並不容易，雖然許多研究已針對組合選擇，設施規劃，產能規劃，生產排程等主要問題進行探討，然而，它們仍然有許多挑戰，需要更有效率和有效的方法來做出聰明製造相關問題的決策。
本研究旨在提出有效的方法來解決在聰明製造環境中所發生的關鍵決策問題，特別是選擇組合在策略層面的決策問題，同時考量問題的獨立性、相關性及綜效。我們建構一個數學模型並應用於這類問題的實證上，此外，還開發了混合自動調整基因演算法來有效地解決問題，在操作層面上，批量排程問題使用一個綜合模型來解決和建構。本研究提出了一個混合多母體基因演算法來解決這個問題，此外，還進行了兩個案例研究來驗證所提出的優化方法，包括IC設計組合選擇和紡織品染色排程。 結果證明了所提出方法的有效性。

Decision making occurs at all levels of an organization and in various domains, from service to manufacturing. The fourth industrial revolution (Industry 4.0) prompts the enterprises to improve their smart manufacturing technology, smart operations management, and advanced analytics in which decision-making models for smart production management play an important role. The methods for making decision vary from problem to problem. Identifying an efficient and effective approach for a specific problem is difficult. Many researches have been done for main problems such as portfolio selection, facility layout, capacity planning, operation scheduling problems, and so on. However, it still remains challenges and needs more efficient and effective methods for making decisions involved in the problems in smart production.
This research aims to address the critical decision-making problems and propose efficient approaches for solving some cases as illustrations of smart production. In particular, the portfolio selection problem at strategic level is addressed in considering the independent, interrelated, and synergistic attributes. A mathematical model is constructed for solving the problem in small instances. Furthermore, a hybrid autotuning genetic algorithm is developed to solve the problem efficiently. At operational level, a batch scheduling problem is addressed and constructed in a comprehensive model. A hybrid multi-subpopulation genetic algorithm is proposed to solve the problem in large instances. Furthermore, two case studies are conducted to validate the proposed optimization approaches including an IC design portfolio selection and textile dyeing scheduling. The results have shown the efficiency of the proposed approaches.

Abiyev, R.H., and Menekay, M. (2007). Fuzzy portfolio selection using genetic algorithm. Soft Computing, 11(12), 1157-1163.
Allahverdi, A. (2015). The third comprehensive survey on scheduling problems with setup times/costs. European Journal of Operational Research, 246(2), 345-378.
Allahverdi, A., Gupta, J. N., and Aldowaisan, T. (1999). A review of scheduling research involving setup considerations. Omega, 27(2), 219-239.
Allahverdi, A., Ng, C. T., Cheng, T. E., and Kovalyov, M. Y. (2008). A survey of scheduling problems with setup times or costs. European Journal of Operational Research, 187(3), 985-1032.
Anderl, R. (2014). Industrie 4.0 - advanced engineering of smart products and smart production. The 19th International Seminar on High Technology, Brazil
Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D., Cochran, J.J., Fry, M.J., and Ohlmann, J.W. (2014). An introduction to management science: Quantitative approaches to decision making. Cengage Learning, Boston, USA.
Arnone, S., Loraschi, A., and Tettamanzi, A. (1993). A genetic approach to portfolio selection. Neural Network World, 6, 597-604.
Balasubramanian, H., Mönch, L., Fowler, J., and Pfund, M. (2004). Genetic algorithm based scheduling of parallel batch machines with incompatible job families to minimize total weighted tardiness. International Journal of Production Research, 42(8), 1621-1638.
Baluja, S. (1994). Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. School of Computer Science, Carnegie Mellon Univ., Pittsburgh, USA, Tech. Rep. CMU-CS-94-163.
Bilyk, A., Mönch, L., and Almeder, C. (2014). Scheduling jobs with ready times and precedence constraints on parallel batch machines using metaheuristics. Computers & Industrial Engineering, 78, 175-185.
Carazo, A. F., Gomez, T., Molina, J., Hernandez-Diaz, A. G., Guerrero, F. M. and Caballero, R. (2010). Solving a comprehensive model for multi-objective project portfolio selection. Computers & Operations Research, 37(4), 630 – 639.
Chamnanlor, C., Sethanan, K., Chien, C.-F., and Gen, M. (2014). Re-entrant flow shop scheduling problem with time windows using hybrid genetic algorithm based on auto-tuning strategy. International Journal of Production Research, 52(9), 2612–2629.
Chang, H. C., Chen, Y. P., Liu, T. K., and Chou, J. H. (2015). Solving the flexible job shop scheduling problem with makespan optimization by using a hybrid Taguchi-genetic algorithm. IEEE Access, 3, 1740-1754.
Chang, P. Y., Damodaran, P., and Melouk, S. (2004). Minimizing makespan on parallel batch processing machines. International Journal of Production Research, 42(19), 4211-4220.
Chen, Y.-J and Chien, C.-F. (2018). An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing. International Journal of Production Research, 1-15.
Cheng, T. E., Gupta, J. N., and Wang, G. (2000). A review of flow shop scheduling research with setup times. Production and Operations Management, 9(3), 262-282.
Chien, C.-F. (2002). A portfolio-evaluation framework for selecting R&D projects. R&D Management, 32(4), 359-368.
Chien, C.-F., and Chen, C.-H (2007). Using GA and CTPN for modeling the optimization-based schedule generator of a generic production scheduling system. International Journal of Production Research, 45(8), 1763-1789.
Chien, C.-F., and Hsu, C.-Y. (2011). UNISON analysis to model and reduce step-and-scan overlay errors for semiconductor manufacturing. Journal of Intelligent Manufacturing, 22, 399–412.
Chien, C.-F., and Huynh, N.-T. (2018). An Integrated Approach for IC Design R&D Portfolio Decision and Project Scheduling and a Case Study. IEEE Transactions on Semiconductor manufacturing, 31(1), 76-86.
Chien, C.-F., and Sainfort, F. (1998). Evaluating the desirability of meals: an illustrative multi-attribute decision analysis procedure to assess portfolios with interdependent items. Journal of Multi-Criteria Decision Analysis, 7(4), 230-238.
Chien, C.-F., Chen, Y.-J., and Peng, J.-T. (2010). Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. International Journal of Production Economics, 128(2), 496-509.
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, 13(4), 1567-1580.
Chien, C.-F., Hong, T.-Y., and Guo, H.-Y. (2017). An empirical study for smart production for TFT-LCD to empower Industry 3.5. Journal of the Chinese Institute of Engineers, 40(7), 552-561.
Chien, C.-F., Hu, C.-H., and Lin, C.-Y. (2008). Analysing inspection frequency for wafer bumping process and an empirical study of UNISON decision framework. Int. J. Manufacturing Technology and Management, 14(1-2), 130-144.
Chien, C.-F., Wang, H.-J., and Wang, M. (2007). A UNISON framework for analysing alternative strategies of IC final testing for enhancing overall operational effectiveness. Int. J. Production Economics, 107, 20–30.
Chien, C.-F., Wu C., and Chiang, Y. (2012). Coordinated capacity migration and expansion planning for semiconductor manufacturing under demand uncertainties. International Journal of Production Economics, 135(2), 860-869.
Chou, C.-W., Chien, C.-F., and Gen, M. (2014). A multi-objective hybrid genetic algorithm for TFT-LCD module assembly scheduling. IEEE Transactions on Automation Science and Engineering, 11(3), 692-705.
Clemen, R.T. (1996). Making hard decisions: An introduction to decision analysis, 2nd edition. Duxbury Press, USA.
Costa, A., Cappadonna, F. A., and Fichera, S. (2017). A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem. Journal of Intelligent Manufacturing, 28(6), 1269-1283.
Črepinšek, M., Liu, S. H., and Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys (CSUR), 45(3), 35.
Davis, D. (1996). Business research for decision making, fourth edition. Duxbury Press, USA.
Deb, K., (2001). Multi-objective optimization using evolutionary algorithms. John Wiley & Sons.
Elmquist, M. and Masson, P.L. (2009). The value of a ‘failed’ R&D project: an emerging evaluation framework for building innovative capabilities. R&D Management, 39(2), 136-152.
Eom, D. H., Shin, H. J., Kwun, I. H., Shim, J. K., and Kim, S. S. (2002). Scheduling jobs on parallel machines with sequence-dependent family set-up times. The International Journal of Advanced Manufacturing Technology, 19(12), 926-932.
Executive Office of the President (2014). Accelerating U.S. advanced manufacturing: report to the President. The President’s Council of Advisors on Science and Technology of the United State.
Federal Ministry of Education and Research (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. National Academy of Science and Engineering of Germany.
Gen, M., and Lin, L. (2014). Multi-objective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey. Journal of Intelligent Manufacturing, 25(5), 849-866.
Gen, M., Chang, R., and Lin, L. (2008). Network models and optimization: Multiobjective genetic algorithm approach. Springer.
Gen, M., Zhang, W., Lin, L., & Yun, Y. (2017). Recent advances in hybrid evolutionary algorithms or multiobjective manufacturing scheduling. Computers & Industrial Engineering, 112, 616-633.
Ghapanchi, A.H., Tavana, M., Khakbaz, M.H., and Low. G. (2012). A methodology for selecting portfolios of projects with interactions and under uncertainty. International Journal of Project Management, 30(7), 791–803.
Ghasemzadeh, F., Archer, N., and Iyogun, P., (1999). A zero-one model for project portfolio selection and scheduling. The Journal of the Operational Research society, 50(7), 745-755.
Ghorbani, S., and Rabbani, M. (2009). A new multi-objective algorithm for a project selection problem. Advances in Engineering Software, 40(1), 9-14.
Giachetti, C. and Dagnino, G.B. (2017). The impact of technological convergence on firms' product portfolio strategy: an information-based imitation approach. R&D Management, 47(1), 17-35.
Graham, R. L., Lawler, E. L., Lenstra, J. K., and Kan, A. R. (1979). Optimization and approximation in deterministic sequencing and scheduling: a survey. Annals of discrete mathematics, 5, 287-326.
Guo, P., Liang, J. J., Zhu, Y. M., and Hu, J. F. (2008). R&D project portfolio selection model analysis within project interdependencies context. IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, 994-998.
Gutjahr, W.J., Katzensteiner, S., Reiter, P., Stummer, C., and Denk, M., (2008). Competence-driven project portfolio selection, scheduling and staff assignment. Central European Journal of Operations Research, 16(3), 281-306.
Hachemi, N., Saddoune, M., Hallaoui, I., and Rousseau, L. M. (2015). A two-phase approach to solve the synchronized bin–forklift scheduling problem. Journal of Intelligent Manufacturing, 1-7.
Handa, H., Kawakami, H., and Katai, O. (2008). Recent advances in evolutionary computation. IEEJ Transactions on Electronics, Information & Systems, 128(3), 334–339.
Heidenberger, K., and Stummer, C. (1999). Research and development project selection and resource allocation: a review of quantitative modeling approaches. International Journal of Management Reviews, 1(2), 197-224.
Hsieh, T. Y. and Liu, H. L. (2004). Genetic algorithm for optimization of infrastructure investment under time-resource constraints. Computer-Aided Civil and Infrastructure Engineering, 19(3), 203–212.
Hsu, H. M., Hsiung, Y., Chen, Y. Z., and Wu, M. C. (2009). A GA methodology for the scheduling of yarn-dyed textile production. Expert Systems with Applications, 36(10), 12095-12103.
Ivanov, D., Dolgui, A., Sokolov, B., Wemer, F., and Ivanova, M. (2015). A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. International Journal of Production Research, 56(2), 386-402.
Jamrus, T., and Chien, C.-F. (2016). Extended priority-based hybrid genetic algorithm for the less-than-container loading problem. Computers & Industrial Engineering, 96, 227-236.
Jiang, W., Yuan, Z., Bi, J., and Sun, L. (2010). Conserving water by optimizing production schedules in the dyeing industry. Journal of Cleaner Production, 18(16), 1696-1702.
Jia, Z. H., and Leung, J. Y. T. (2015). A meta-heuristic to minimize makespan for parallel batch machines with arbitrary job sizes. European Journal of Operational Research, 240(3), 649-665.
Joo, C. M., and Kim, B. S. (2015). Hybrid genetic algorithms with dispatching rules for unrelated parallel machine scheduling with setup time and production availability. Computers & Industrial Engineering, 85, 102-109.
Kao, C., and Hung, H. T. (2005). Data envelopment analysis with common weights: the compromise solution approach. Journal of the Operational Research Society, 56(10), 1196-1203.
Karafotias, G., Hoogendoorn, M., and Eiben, A. E. (2015). Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans. Evolutionary Computation, 19(2), 167-187.
Keeney, R. L. (1987). An analysis of the portfolio of sites to characterize for selecting a nuclear repository. Risk Analysis, 7(2), 195-216.
Khalili-Damghani, K., Sadi-Nezhad, S., Lotfi, F.H., and Tavana, M. (2013). A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection. Information Sciences, 220, 442–462.
Kim, K.W., Yun, Y.S., Yoon, Y.M., Gen, M. and Yamazaki, G. (2005). Hybrid genetic algorithm with adaptive abilities for resource-constrained multiple project scheduling. Computers in Industry, 56(2), 143–160.
Koh, S. G., Koo, P. H., Ha, J. W., and Lee, W. S. (2004). Scheduling parallel batch processing machines with arbitrary job sizes and incompatible job families. International Journal of Production Research, 42(19), 4091-4107.
Lee, C.-Y. and Chien, C.-F. (2014). Stochastic programming for vendor portfolio selection and order allocation under delivery uncertainty. OR Spectrum, 36(3), 761-797.
Lee, H., and Maravelias, C.T., (2017). Mixed-integer programming models for simultaneous batching and scheduling in multipurpose batch plants. Computers & Chemical Engineering, 106, 621-644.
Li, X., and Gao, L. (2016). An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. International Journal of Production Economics, 174, 93-110.
Lin, C.C. and Liu Y.T. (2008). Genetic algorithms for portfolio selection problems with minimum transaction lots. European Journal of Operational Research, 185(1), 393–404.
Lin, Y.-H, Chien, C.-F., and Yu, C.-M. (2015). UNISON decision analysis framework for workforce planning for semiconductor Fabs and an empirical study. International Journal of Industrial Engineering, 22(5), 631-644.
Liu, S.S. and Wang, C.J. (2011). Optimizing project selection and scheduling problems with time-dependent resource constraints. Automation in Construction, 20(8), 1110–1119.
Marakas, G.M. (1999). Decision support system in the 12th century. Prentice-Hall, USA.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.
Mosterman, P.J. and Zander, J. (2015). Industry 4.0 as a Cyber-Physical System study. Software and System Modeling, 15(1), 17-29.
Mönch, L., Balasubramanian, H., Fowler, J. W., and Pfund, M. E. (2005). Heuristic scheduling of jobs on parallel batch machines with incompatible job families and unequal ready times. Computers & Operations Research, 32(11), 2731-2750.
Naderi, B. (2013). The project portfolio selection and scheduling problem: mathematical model and algorithms. Journal of Optimization in Industrial Engineering, 6(13), 65-72.
Oliveira, M.G., Rozenfeld H., Phaal R., and Probert D. (2015). Decision making at the front end of innovation: the hidden influence of knowledge and decision criteria. R&D Management, 45(2), 161-180.
Omrani, H. (2013). Common weights data envelopment analysis with uncertain data: A robust optimization approach. Computers & Industrial Engineering, 66(4), 1163-1170.
Perez, F., and Gomez, T. (2016). Multi-objective project portfolio selection with fuzzy constraints. Annals of Operations Research, 245, 7-29.
Pidun, U. (2010). “Value-based management of the innovation portfolio” in innovation and international corporate growth. Springer, London, 281-297.
Potts, C. N., and Kovalyov, M. Y. (2000). Scheduling with batching: a review. European journal of operational research, 120(2), 228-249.
Rafiee, M., Kianfar, F., and Farhadkhani, M. (2014). A multistage stochastic programming approach in project selection and scheduling. The International Journal of Advanced Manufacturing Technology, 70, 2125–2137.
Saydam, C., and Cooper, W. D. (2002). A decision support system for scheduling jobs on multi-port dyeing machines. International Journal of Operations & Production Management, 22(9), 1054-1065.
Schuster, C. J., and Framinan, J. M. (2003). Approximative procedures for no-wait job shop scheduling. Operations Research Letters, 31(4), 308-318.
Shen, L., Mönch, L., and Buscher, U. (2014). A simultaneous and iterative approach for parallel machine scheduling with sequence-dependent family setups. Journal of Scheduling, 17(5), 471-487.
Shrouf, F., Ordieres, J., and Miragliotta, G. (2014). Smart factories in industry 4.0: a review of the concept and of energy management approached in production based on the Internet of Things paradigm. IEEE International conference on IEEM, Malaysia.
Stummer, C. and Heidenberger, K. (2003). Interactive R&D portfolio analysis with project interdependencies and time profiles of multiple objectives. IEEE Trans Eng Management, 50(2), 175–83.
Sun, H., and Ma, T. (2005). A packing-multiple-boxes model for R&D project selection and scheduling. Technovation, 25(11), 1355–1361.
Tofighian, A.A. and Naderi, B. (2015). Modeling and solving the project selection and scheduling. Computers & Industrial Engineering, 83, 30–38.
Uzsoy, R. (1994). Scheduling a single batch processing machine with non-identical job sizes. The international journal of operation research, 32(7), 1615-1635.
Veldhuizen V.D. (1999). Multiobjective evolutionary algorithms: classification, analyses and new innovations. Doctoral dissertation, Air Force Institute of Technology.
Wang, H.-K., Chien, C.-F., & 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, 114, 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. 28(3), 353-366.
Wang, L., and Fang, C. (2012). An effective estimation of distribution algorithm for the multi-mode resource-constrained project scheduling problem. Computers & Operations Research, 39(2), 449-460.
Wang, L., and Zheng, D. Z. (2001). An effective hybrid optimization strategy for job-shop scheduling problems. Computers & Operations Research, 28(6), 585-596.
Wang, L., Fang, C., Mu, C. D., and Liu, M. (2013). A Pareto-archived estimation-of-distribution algorithm for multi-objective resource-constrained project scheduling problem. IEEE Transactions on Engineering Management, 60(3), 617-626.
Wang, L., Wang, S., Xu, Y., Zhou, G., and Liu, M. (2012). A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem. Computers & Industrial Engineering, 62(4), 917-926.
Wang, S.M., Chang, J.C., Wee, H.M., and Wang, K.J. (2005). Non-linear stochastic optimization using genetic algorithm for portfolio selection. International Journal of Operations Research, 3(1), 16-22.
Wu, J.-Z., Chien, C.-F., and Gen, M. (2012). Coordinating strategic outsourcing decisions for semiconductor assembly using a bi-objective genetic algorithm, International Journal of Production Research, 50(1), 235-260.
Wu, J.-Z., Hao, X.-C., Chien, C.-F., and Gen, M. (2012). A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem. Journal of Intelligent Manufacturing, 23(6), 2255-2270.
Yu, X. and Gen, M. (2010). Introduction to evolutionary algorithms, Springer, London.
Yu, L., Wang, S., Wen, F., and Lai, K.K. (2012). Genetic algorithm-based multi-criteria project portfolio selection. Annals of Operations Research, 197(1), 71-86.