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Multi-objective cell formation and production planning in dynamic virtual cellular manufacturing systems

机译:动态虚拟细胞制造系统中的多目标细胞形成和生产计划

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This article presents a fuzzy goal programming-based approach for solving a multi-objective mathematical model of cell formation problem and production planning in a dynamic virtual cellular manufacturing system. In a dynamic environment, the product mix and part demand change over a planning horizon decomposed into several time periods. Thus, the cell formation done for one period may be no longer efficient for subsequent periods and hence reconfiguration of cells is required. Due to the variation of demand and necessity of reconfiguration of cells, the virtual cellular manufacturing (VCM) concept has been proposed by researchers to utilise the benefits of cellular manufacturing without reconfiguration charges. In a VCM system, machines, parts and workers are temporarily grouped for one period during which machines and workers of a group dedicatedly serve the parts of that group. The only difference of VCM with a real CM is that machines of the same group are not necessarily brought to a physical proximity in VCM. The virtual cells are created periodically depending on changes in demand volumes and mix, as new parts accumulate during a planning horizon. The major advantage of the proposed model is the consideration of demand and part mix variation over a multi-period planning horizon with worker flexibility. The aim is to minimise holding cost, backorder cost and exceptional elements in a cubic space of machine-part-worker incidence matrix. To illustrate the applicability of the proposed model, an example has been solved and computational results are presented.
机译:本文提出了一种基于模糊目标编程的方法,用于解决动态虚拟细胞制造系统中细胞形成问题和生产计划的多目标数学模型。在动态环境中,产品组合和零件需求会在分解为多个时间段的计划范围内变化。因此,在一个时期内完成的细胞形成对于随后的时期可能不再有效,因此需要重新配置细胞。由于需求的变化和重新配置单元的必要性,研究人员提出了虚拟蜂窝制造(VCM)概念,以利用无需重新配置费用的蜂窝制造优势。在VCM系统中,将机器,零件和工人临时分组一段时间,在此期间,一组中的机器和工人专门为该组的零件服务。 VCM与真实CM的唯一区别在于,不必将同一组中的计算机置于VCM中的物理位置附近。随着新零件在计划范围内的积累,会根据需求量和混合的变化定期创建虚拟单元。该模型的主要优点是考虑了在多阶段计划范围内需求和零件混合的变化,并具有工人的灵活性。目的是在机器零件工人关联矩阵的立方空间中最小化持有成本,缺货成本和特殊元素。为了说明所提出模型的适用性,已经解决了一个例子,并给出了计算结果。

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