| Summary: | 碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 102 === In this thesis, we investigate the open-shop scheduling problem for tasks with multiple sequential operations in which a variety of tasks need to be processed and scheduled. There are many applications for the considered problem. In this thesis, we will explore three types of open-shop scheduling problems for tasks with multiple sequential operations, including aircraft maintenance scheduling problem, physical examination scheduling problem and administrative procedure scheduling problem.
The considered open-shop scheduling problem for tasks with multiple sequential operations is an extension of the typical open-shop scheduling problems. Since typical open-shop scheduling problem is an NP-hard problem, the considered scheduling problem is also an NP-hard problem. As known, typical approaches require much of time for solving the considered problem, and they cannot guarantee the quality of solutions.
Generally, artificial intelligence algorithms can be used to solve for the solutions of considered problem. Though they cannot guarantee the global optimal solutions, they can provide effective solutions within a reasonable CPU time. In this thesis, we attempt to adopt artificial intelligence algorithms to solve the considered problem.
In this thesis, three artificial intelligence algorithms, including genetic algorithm, immune algorithm and particle swarm optimization algorithm, are applied for solving the considered problem. The objective of the considered problem is to minimize the completion time. In this study, numerical results by these three algorithms are reported, compared and analyzed. Experimental results show that immune algorithm is superior to the other two algorithms in solution quality. However, genetic algorithm is more efficienct than the other two algorithms.
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