Data mining based storage assignment heuristics for travel distance reduction



Among the warehousing activities in distribution centres, order picking is the most time-consuming and labour-intensive. As a result, order picking may become a bottleneck preventing distribution centres from maximizing the effectiveness of their warehousing activities. Although storage location assignment (or product allocation) is a tactical decision, it is especially influential on the effectiveness of order picking. In previous studies, most storage assignment approaches considered the order frequency of individual products rather than that of product groups, which often are purchased together. This paper proposes a new association measure, weighted support count (WSC), based on association rule mining, to represent both the intensity and nature of the relationships between products in a distribution centre. This paper presents two storage assignment heuristics, the modified class-based heuristic (MCBH) and the association seed based heuristic (ASBH), designed to facilitate efficient order picking by applying WSC. The real-world data set of a grocery distribution centre is used to verify the effectiveness of the proposed approaches. From the computational results, MCBH cuts at most 4% from the travel distance for order picking per month, as compared with the traditional class-based approach. Meanwhile, ASBH achieves at most a 13% reduction in travel distance.