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Evolutionary computation approaches to cell optimisation

机译:细胞优化的进化计算方法

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This paper examines a cellular manufacturing optimisation problem in a new facility of a pharmaceutical company. The new facility, together with the old one, should be adequate to handle current and future production requirements. The aim of thispaper is to investigate the potential use of evolutionary computation in order to find the optimum configuration of the cells in the facility. The objective is to maximise the total number of batches processed per year in the facility. In addition, atwo-objective optimisation search was implemented, using several evolutionary computation methods. One additional objective is to minimise the overall cost, which is proportional to the number of cells in the facility. The multiobjective optimisationprograms were based on three approaches: The weighted-sum approach, the Pareto-optimality approach, and the Multiobjective Genetic Algorithm (MOGA) approach.
机译:本文探讨了制药公司新设施中的蜂窝制造优化问题。新设施与旧工厂合作,应充分处理当前和未来的生产要求。此纸纸的目的是调查进化计算的潜在使用,以便找到设施中细胞的最佳配置。目标是最大化设施中每年处理的批次总数。此外,使用若干进化计算方法实现了ATWO目标优化搜索。一个额外的目的是最小化整体成本,其与设施中的细胞数量成比例。多目标优化应用程序基于三种方法:加权和方法,静态最优法和多目标遗传算法(MOGA)方法。

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