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Machine-cell and Part-family Formation in Cellular Manufacturing Using a Two-phase Clustering Algorithm

机译:使用双相聚类算法蜂窝制造中的机器单元和部分系列

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This paper presents a two-phase clustering algorithm for machine-cell and part-family formation in the design of cellular manufacturing systems. The proposed algorithm begins with the determination of initial cluster centers via a linear assignment method using the least similar group representatives in its first phase. A fuzzy C-means clustering method is followed in its second phase for part-family and machine-cell formation using the obtained initial cluster centers. The two-phase algorithm can remedy the problem of clustering inconsistency resulting from the fuzzy C-means method with random initializations. Experimental results on many benchmark data sets based on multiple performance criteria substantiate the effectiveness of the proposed algorithm.
机译:本文介绍了蜂窝制造系统设计中的机器单元和部分系列的两相聚类算法。所提出的算法首先使用其第一阶段中的最低相似组代表的线性分配方法确定初始聚类中心。采用所获得的初始聚类中心,在其第二阶段进行模糊C-Means聚类方法,用于部分家庭和机器单元的形成。两阶段算法可以弥补具有随机初始化的模糊C-均值方法群体群集不一致的问题。基于多种性能标准的许多基准数据集的实验结果证实了所提出的算法的有效性。

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