| Summary: | 碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 96 === As a typical manufacturing and scheduling problem with strong industrial background, flow shop scheduling with limited buffers has gained wide attention both in academic and engineering fields. However, this operation results the heavy burden on some machine and late completion time of the job, and therefore delay the makespan. The objective of the flow shop scheduling problem is to minimize the total completion time (or makespan) of jobs. In this paper, there are two main parts, namely：
Part 1. An effective Immune Algorithm (IA) is proposed to permutate flow shop scheduling with limited buffers. In the IA, not only multiple genetic operators based on evolutionary mechanism are used simultaneously in hybrid sense, but also a neighborhood structure based on graph model is employed to enhance the local search, so that the exploration and exploitation abilities can be well balanced. For this proposed IA, we test 29 well known benchmark problems to evaluate its performance. Numerical results show that the proposed IA is superior to other typical approaches, e.g., genetic algorithm, for all test problems.
Part 2. We study the effects of deterioration of jobs in the Flow Shop scheduling with the use of the proposed IA. We test 25 problems and discuss the results.
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