The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres

TLDR

Order picking is the most labor‑intensive activity in distribution centres, and storage assignment policies have been studied to reduce travel distance, but prior work has focused on locating entire products from scratch. This study proposes an adaptive, data‑mining‑based storage assignment (DMSA) approach to determine optimal placement for newly delivered products when shelf space is available. DMSA introduces a new association index (AIX) derived from association rule mining to evaluate product‑location fitness, formulates the storage location assignment problem as a binary integer program, and is tested on a real‑world order database against random assignment. Results show that DMSA outperforms random assignment, particularly as the number of put‑away products and the proportion of high‑turnover items increase. Keywords: enterprise information systems, business planning and logistics, warehousing, storage assignment, order picking, data mining, association rules.

Abstract

Abstract 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. Keywords: enterprise information systemsbusiness planning and logisticswarehousingstorage assignmentorder pickingdata miningassociation rules