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Efficient formation of storage classes for warehouse storage location assignment: A simulated annealing approach

机译:用于仓库存储位置分配的有效存储类形成:一种模拟退火方法

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摘要

Class-based storage policy distributes products among a number of classes and for each class it reserves a region within the storage area. The procedures reported in the literature for formation of storage classes primarily consider order-picking cost ignoring storage-space cost. Moreover, in these procedures items are ordered on the basis of their cube per order index (COI), and items are then partitioned into classes maintaining this ordering. This excludes many possible product combinations in forming classes which may result in inferior solutions. In this paper, a simulated annealing algorithm (SAA) is developed to solve an integer programming model for class formation and storage assignment that considers all possible product combinations, storage-space cost and order-picking cost. Computational experience on randomly generated data sets and an industrial case shows that SAA gives superior results than the benchmark dynamic programming algorithm for class formation with COI ordering restriction.
机译:基于类别的存储策略将产品分配到多个类别中,并且对于每个类别,它都会在存储区域内保留一个区域。文献中报道的用于形成存储类别的过程主要考虑了订单拣选成本,而忽略了存储空间成本。此外,在这些过程中,将根据每个订单索引(COI)的多维数据集对项目进行排序,然后将项目划分为多个类,以保持此排序。这排除了形成类别的许多可能的产品组合,这些组合可能导致劣等的解决方案。在本文中,开发了一种模拟退火算法(SAA),以解决用于类别形成和存储分配的整数规划模型,该模型考虑了所有可能的产品组合,存储空间成本和拣货成本。对随机生成的数据集的计算经验和一个工业案例表明,对于具有COI排序限制的类形成,SAA的结果优于基准动态编程算法。

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