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Application of fuzzy materials-handling inputs with the genetic algorithm for machine layout

机译:遗传算法在模糊物料处理输入中的应用

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摘要

Each manufacturing industry produces a variety of products. Each product is different in terms of materials, process and production flow. These factors affect the total material handling, but the real situation has vagueness or fuzziness in the manufacturing system, depending on the periods of time involved, products variety, and business growth. A proper machine layout reduces material handling cost. Therefore, machine layout planning involved with fuzzy method is be applied to maximum utilization of facilities available. This paper presents the application of fuzzy set theory with a genetic algorithm for the selection operator using tournament selection for solving machine layout problems in order to manage vague information. Parameters including material flow frequency and material flow volume between workstations were considered as fuzzy numbers. The results were compared with a case study in a manufacturing system. The total distances had been from 38.45 m. to 26.25 m. 31.73% distance had been shorten. Therefore, the proposed method was able to solve machine layout problems effectively. Thus, it can be an alternative method in manufacturing management.
机译:每个制造业生产各种产品。每种产品的材料,工艺和生产流程都不同。这些因素影响物料的整体处理,但是实际情况在制造系统中会模糊不清,这取决于所涉及的时间段,产品种类和业务增长。正确的机器布局可降低材料处理成本。因此,将涉及模糊方法的机器布局规划应用于最大程度地利用可用设施。本文介绍了一种具有遗传算法的模糊集理论在选择算子上的应用,该算子使用锦标赛选择来解决机器布局问题,从而管理模糊信息。工作站之间的物料流动频率和物料流动量等参数被视为模糊数。将结果与制造系统中的案例研究进行了比较。总距离为38.45 m。至26.25 m。距离缩短了31.73%。因此,所提出的方法能够有效地解决机器布局问题。因此,它可以是制造管理中的替代方法。

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