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A hybrid GA/PSO for the concurrent design of cellular manufacturing system

机译:用于蜂窝制造系统并发设计的混合GA / PSO

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In this paper, a hybrid search algorithm using genetic algorithm (GA) and particle swarm optimization (PSO) is implemented for the concurrent design of cellular manufacturing system. Traditionally, cell formation (CF) and group layout (GL) problems were considered sequentially therefore the results may be optimal in one phase but during implementation of the whole cellular manufacturing, it may not be globally optimal. Based on the studies by earlier researchers concurrent approach does indeed lead to better solution quality than the sequential approach by a magnitude of 2% to 20%. Three performance measures are considered to evaluate the proposed method. They are to minimize total inter-cell and intra-cell moves, total cell load variation and total inter cell moves of part families. The performance of the proposed hybrid GA/PSO is evaluated with the test problems available in the literature. The results obtained clearly indicate the better performance of the proposed heuristic.
机译:本文实现了一种使用遗传算法(GA)和粒子群优化(PSO)的混合搜索算法用于蜂窝制造系统的并发设计。传统上,依次考虑细胞形成(CF)和组布局(GL)问题,因此结果可以在一期中最佳,但在整个细胞制造期间,它可能不是全局最佳的。基于之前的研究,研究人员并发方法确实导致更好的解决方案质量比顺序方法的幅度为2%至20%。三种性能措施被认为是评估所提出的方法。它们是最小化总细胞间和内部电池移动,总细胞负载变化和部件家庭的总帧间移动。通过文献中可用的测试问题评估所提出的杂交GA / PSO的性能。获得的结果清楚地表明拟议启发式的表现更好。

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