| Summary: | 碩士 === 正修科技大學 === 工業工程與管理研究所 === 95 === The scheduling concept and skills are implemented in our dally life such as the people waiting for services, and the timetable of train. However, in some reality scheduling cases are analogized to the open shop problem. For example a maintenance processes of automobile, a physical examination processes and a registration processes of entrance, their routing processes are not necessary be fulfilled in order. Open shop problem generally assume the processed part have the same weights, which do not adapt the reality situation, like the emergency room was supposed to deal the most suffered patient with preference. The open shop problem are known to be NP-hard and it hard to be solved to the optimum by neither branch and bound nor linear programming approach. Therefore, in this study, a robust genetic algorithm approach is developed and implemented to find the solution of open shop problem with minimizing weighted total completion time of all job. A computational experiment adopting 36 literature problem is conducted to examine the effectiveness of the proposed approach. The computational results reveal, compared to the lower bound, there is 7.13% deviation in small-sized problem, 13.84% deviation in medium-sized problem. This developed approach can be improved by involving the characteristics of open shop to enhance its effectiveness.
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