| Summary: | 碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 95 === Scheduling is widely used in many fields, like information engineering, manufacture, production management and so on. A good scheduling can save time and reduce cost without decreasing the satisfaction of customers. The “flow-shop scheduling” is the most common problem in the daily life. For this specific problem, there will be n! feasible solutions when we sequence all of the jobs by flow-shop scheduling problems, and this will become a NP-complete problems in mathematics. In other words, the complexity of finding the solutions will be increased with the number of the elements, and it will almost be impossible to find the optimal solution in a short time. Recently, the heuristics algorithms have become the most popular ones to find the optimal solutions of the NP-complete problems, and many literatures in the area had been published.
In this paper we application the genetic algorithms (GA) to find the optimal solutions of the flow-shop scheduling problems and shows the comparisons of the results of makespan which derived from the GA method and similar particle swarm optimization algorithms (SPSOA) and cross-entropy method (CE).
It has been found that the proposed algorithm, genetic algorithms, is better than other heuristics ones when finding the optimal approximate solutions of the flow-shop scheduling problems.
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