The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres
The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres
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DOI：
10.1080/17517575.2010.537784
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发表时间：
2011-01-01
影响因子：
4.4
通讯作者：
Chen, Mu-Chen
中科院分区：
文献类型：
--
作者：
Chiang, David Ming-Huang;Lin, Chia-Ping;Chen, Mu-Chen
据报道，在配送中心的运作中，挑选订单是劳动密集度最高的活动。在文献中已经探索了采用复杂的存储分配策略来减少订单挑选的旅行距离。不幸的是，之前的研究一直致力于从零开始定位整个产品。相反，这项研究打算提出一种自适应的方法，基于数据挖掘的存储分配方法(DMSA)，以找到当配送中心有空置货架空间时需要存放的新交付产品的最优存储分配。在DMSA中，通过应用关联规则挖掘，提出了一种新的关联指数(AIX)来评估出库产品与未分配的存储位置之间的适合度。使用AIX，存储位置分配问题(SLAP)可以用二进制整数规划来表示和求解。为了评估DMSA的性能，获得了一个配送中心的真实订单数据库，并将DMSA的结果与随机分配方法进行了比较。结果表明，随着出库产品数量和周转率较高的出库产品比例的增加，DMSA的绩效优于随机分配。
Among distribution centre operations, order picking has been reported to be the most labour-intensive activity. Sophisticated storage assignment policies adopted to reduce the travel distance of order picking have been explored in the literature. Unfortunately, previous research has been devoted to locating entire products from scratch. Instead, this study intends to propose an adaptive approach, a Data Mining-based Storage Assignment approach (DMSA), to find the optimal storage assignment for newly delivered products that need to be put away when there is vacant shelf space in a distribution centre. In the DMSA, a new association index (AIX) is developed to evaluate the fitness between the put away products and the unassigned storage locations by applying association rule mining. With AIX, the storage location assignment problem (SLAP) can be formulated and solved as a binary integer programming. To evaluate the performance of DMSA, a real-world order database of a distribution centre is obtained and used to compare the results from DMSA with a random assignment approach. It turns out that DMSA outperforms random assignment as the number of put away products and the proportion of put away products with high turnover rates increase.