Data mining-based algorithm for storage location assignment in a randomised warehouse

Data mining has long been applied in information extraction for a wide range of applications such as customer relationship management in marketing. In the retailing industry, this technique is used to extract the consumers buying behaviour when customers frequently purchase similar products together; in warehousing, it is also beneficial to store these correlated products nearby so as to reduce the order picking operating time and cost. In this paper, we present a data mining-based algorithm for storage location assignment of piece picking items in a randomised picker-to-parts warehouse by extracting and analysing the association relationships between different products in customer orders. The algorithm aims at minimising the total travel distances for both put-away and order picking operations. Extensive computational experiments based on synthetic data that simulates the operations of a computer and networking products spare parts warehouse in Hong Kong have been conducted to test the effectiveness and applicability of the proposed algorithm. Results show that our proposed algorithm is more efficient than the closest open location and purely dedicated storage allocation systems in minimising the total travel distances. The proposed storage allocation algorithm is further evaluated with experiments simulating larger scale warehouse operations. Similar results on the performance comparison among the three storage approaches are observed. It supports the proposed storage allocation algorithm and is applicable to improve the warehousing operation efficiency if items have strong association among each other.