| Summary: | 博士 === 國立清華大學 === 工業工程與工程管理學系所 === 106 === 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.
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