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A Discrete Bacterial Chemotaxis Approach to the Design of Cellular Manufacturing Layouts

机译:细胞制造布局设计的离散细菌趋化性方法。

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The design of cellular manufacturing layouts is a very important process, because an adequate placement of machines can reduce costs and waiting times, and ultimately improve the yield of the system. The design process includes two main optimization sub-problems. The first one is a clustering problem, the so-called cell formation, consisting in the definition of groups (the cells) of machines that produce sets of related product parts. The second step is a location-allocation problem, which has to be solved to define the relative position of the cells and of the machines inside each cell. Both problems offer significant challenges from a computational point of view. This paper presents a novel approach for the design of cellular manufacturing layouts through an optimization algorithm based on bacterial chemotaxis. The proposed approach solves simultaneously the two optimization sub-problems mentioned above by minimizing transport cost and maximizing clustering of cells, taking into account the sequencing of production steps, the volume of production and the batch sizes. The performance of the proposed algorithm was tested through benchmark problems, and the results were compared with a genetic algorithm and analytical solutions modeled in GAMS. In all cases our proposal achieves better performance than Genetic Algorithm in quality and time, and comparable results with exact analytical solutions.
机译:蜂窝制造布局的设计是非常重要的过程,因为适当放置机器可以减少成本和等待时间,并最终提高系统的产量。设计过程包括两个主要的优化子问题。第一个是集群问题,即所谓的单元形成,包括定义生产相关产品零件集的机器组(单元)的定义。第二步是位置分配问题,必须解决该问题才能定义单元和每个单元内部机器的相对位置。从计算的角度来看,这两个问题都提出了严峻的挑战。本文提出了一种通过基于细菌趋化性的优化算法设计细胞生产布局的新颖方法。考虑到生产步骤的顺序,生产量和批量大小,所提出的方法通过最小化运输成本和最大化细胞簇,同时解决了上述两个优化子问题。通过基准问题测试了该算法的性能,并将结果与​​遗传算法和GAMS建模的解析解进行了比较。在所有情况下,我们的建议在质量和时间上均比遗传算法具有更好的性能,并且在精确的分析解决方案上具有可比结果。

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