| Summary: | 碩士 === 國立臺灣大學 === 工業工程學研究所 === 107 === This paper defines Flexible Job and Material Delivery Scheduling Problem and uses genetic algorithm to construct a solving method, effectively reduce the maximal completion time of products in the system. Before simulating a schedule, the production process sequence of each product, the different candidate machines and processing time, and the handling time of handling equipment are known. The goal is to minimize product maximal completion time by planning an optimal set of processing operations, selected machines and handling equipment. However, the calculation of product maximal completion time can be evaluated only when detailed schedules of processing operations, machines and handling equipment are available. This work derives a concise simulation algorithm to generate a schedule in the production system. Our research proposes not only greedy heuristic method but also genetic algorithm to solve this problem. This study also attempts to add an appropriate proportion of greedy solution in the initial solution of the genetic algorithm to improve the efficiency of the solution. In order to verify the effectiveness of each methods, the academic literature (Liang et al., 2012) and another large-scale complex standard problem was verified. After applying this problem to test several benchmarks, the results shows that our research can significantly reduce the maximal completion time of products and obtain a great solution. The results also has a better performance than greedy heuristic method. In addition, there are different machine layout and different processing characteristics of product in practice. Our research tests the analysis of handling equipment in different situations and provides users with a decision-making tool.
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