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Emergency Supplies Center Location Clustering Model Based on Imperialist Competitive Algorithm

机译:基于帝国主义竞争算法的应急物资中心选址聚类模型

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In this paper, 2017's hurricane relief in Puerto Rico as the background, a k-means clustering model based on constrained multi-objective multi-sourced weber problem is introduced to determine the optimal locations of emergency material centers, which minimizes distance between road points and emergency supplies centers and weighted distance between hospitals and emergency supplies centers. To effectively solve the model aforementioned, a novel imperialist competitive algorithm (ICA) is proposed which compares two solutions with the lexicographical method. Finally, the results of real data are given and show the effectiveness in solving the problem.
机译:本文以2017年波多黎各的飓风救济为背景,引入了基于约束多目标多源Weber问题的k均值聚类模型来确定应急物资中心的最佳位置,从而最大程度地减少了道路点之间的距离。应急物资中心以及医院与应急物资中心之间的加权距离。为了有效地解决上述模型,提出了一种新的帝国主义竞争算法(ICA),该算法将两种解决方案与词典方法进行了比较。最后,给出了真实数据的结果,并显示了解决该问题的有效性。

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