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A Revised Weighted Fuzzy C-Means and Center of Gravity Algorithm for Probabilistic Demand and Customer Positions

机译:概率需求和客户职位的修订加权模糊C型均值和重心算法

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This study proposes four probabilistic fuzzy c-means algorithms which include a probabilistic fuzzy c-means algorithm (Probabilistic FCM), a probabilistic revised weighted fuzzy c-means algorithm (Probabilistic RWFCM) and hybrid algorithms that combine these algorithms with the center of gravity methods for the un-capacitated planar multi-facility location problem when customer positions and customer demands are probabilistic with predetermined service level. The performance of proposed algorithms was tested with 13 data sets and compared with each other. Experimental results indicate that Probabilistic RWFCM-COG algorithm performs better than other compared algorithms in terms of cost minimization.
机译:本研究提出了四种概率模糊C型算法,包括概率模糊C型算法(概率FCM),概率修正的加权模糊C型算法(概率RWCM)和混合算法,将这些算法与重心方法结合起来 对于未衔接的平面的多设施位置问题,当客户职位和客户需求是具有预定服务级别的概率。 用13个数据集测试所提出的算法的性能,并彼此比较。 实验结果表明,在成本最小化方面,概率RWFCM-COG算法比其他比较算法更好。

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