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Gravity Clustering: A Correlated Storage Location Assignment Problem Approach

机译:重力聚类:相关存储位置分配问题方法

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Warehouses and warehouse-related operations have long been a field of interest for researchers. One of the areas that researchers focus on is the Storage Location Assigning Problem (SLAP or Slotting). The goal in this field is to find the best location in a warehouse to store the products. With the current COVID-19 pandemic, there is a shopping paradigm shift towards e-commerce, which even after the pandemic will not return to the old state. This paradigm shift raises the need for better performing multi-pick warehouses. In this paper, we propose a clustering method based on the gravity model. We show that for warehouses in which there is more than one pick per trip, our proposed method improves the performance.
机译:仓库和仓库相关的业务长期以来一直是研究人员的兴趣领域。研究人员专注的领域之一是分配问题的存储位置(Slap或Slotting)。该领域的目标是在仓库中找到最佳位置来存储产品。随着目前的Covid-19大流行,朝着电子商务的购物范式转变,即使大流行后不会返回旧州。此范例转变提高了更好地执行的多选仓库。在本文中,我们提出了一种基于重力模型的聚类方法。我们表明,对于每次旅行有多个选择的仓库,我们提出的方法可以提高性能。

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