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

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 is conducted based on the number of times two items are picked together and the picking frequency of items, respectively and embed the results into our optimization model. Also, a greedy heuristic is developed to solve SLAP of the firm. According to obtained output, the distances between filled slots and the I/O point of the current system and our proposed solution are compared in order to see the improvement in the system, and an improvement of up to 49.99% is observed.