Data driven storage location assignment problem considering order picking frequencies: A heuristic approach

Abstract

Warehouses are crucial in supply chain management They are used to distribute and store products. In this study, we optimize storage location assignment decisions in a warehouse managed by a manufacturing firm. A mathematical model is introduced to solve the nonlinear mixed integer optimization problem (NLMIP), i.e., the Storage Location Assignment Problem (SLAP) by using historical data from warehouse management system (WMS). Clustering and ABC analysis are conducted based on the number of times two items are picked together and the picking frequency of items, respectively and results are embedded into our optimization model. Also, a greedy heuristic is developed to solve SLAP of the firm. Analysis results show that there is an improvement of up to 49.99% in total distances between filled slots and the I/O point due to proposed solution compared to that of the current system.