| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 96 === Due to the product design have becoming variety and complexity. The enterprises in order to enhance the competitiveness use the multi-plant planning. The assembly sequence in multi-plant planning, it not only considers the assembly sequence to each component but also assign the component to a feasible plant. Therefore, the single-plant planning for assembly is no longer applied to the problem. How to solve the assembly sequence problem quickly in multi-plant planning has been a key issue.
In this thesis, using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) solve the assembly sequence problem in multi-plant planning. The objective is to obtain optimum the assembly sequence in multi-plant for the minimum cost. The assembly sequence problem in multi-plant planning combines the sequence problem and the assignment problem. We using GA solve two problems at same time by a special encoding rule. And in PSO, we using different definition at multi-dimension in order to solve assignment and sequence problem at same time. Cases are given to show the effectiveness of two algorithm can find the solutions which close to optimal, and characteristics of the algorithm are discussed.
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