| Summary: | 碩士 === 國立臺灣大學 === 工業工程學研究所 === 107 === Due to Industrial 4.0 and Internet of Things, we have enough information to predict the machine health. If the machine condition becomes worse, the process will need additional processing time. Therefore, the additional processing time and the machine failure rate must be considered simultaneously when scheduling. Based on these conditions, this study tries to find the optimal preventive maintenance and jobs schedule on parallel machines, while minimize the expected total completion time.
This study first proposed a mathematical programming model to solve this scheduling problem, considering failure rate, the age of machine and additional processing time, trying to minimize the expected completion time. Since this model is a mixed-integer nonlinear mathematical programming model, when the problem size increases, it will not be able to find the optimal solution in a finite computation time. Therefore, this study proposed two heuristic algorithms. For middle scale problem, we proposed two-phase heuristic algorithm, which obtains a local optimal solution with good quality in limited computation time. On the other hand, for large scale problem, we proposed double heuristic algorithm, which efficiently find a feasible solution.
Lastly, this study conducted numerical analysis to analyze the efficiency of the model and algorithm. The result reveals that the two-stage algorithm can effectively assign jobs to each machine and obtain the solution with high quality. Besides, double heuristic algorithm can efficiently solve the problem with 50 jobs.
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