Scattered storage for retail e-commerce fulfillment warehouses with consideration for product turnover

Storage assignment policy of a warehouse controls how the stocks are assigned to various storage locations. Traditionally used storage policies, such as dedicated or random storage, do not address the specific needs of retail e-commerce fulfillment warehouses that store a wide assortment of products and service a large number of single-unit orders under strict timelines. This has led to the development of scattered storage assignment policy, which deliberately spreads the stocks of every product across the warehouse. However, the available approaches towards scattering have largely ignored product turnover, which is a significant determinant of order picking travel. To fill this research gap, we propose a new, turnover-incorporated scattered storage policy. More specifically, we develop a new measure of scattering of stocks and a mathematical model that optimizes the measure by scattering the stocks across the warehouse, while considering product turnover. Certain important properties of this measure are proved and eventually exploited in developing a heuristic approach to solve the proposed mathematical model for large problem instances. An upper bound for the objective function of the mathematical model is used as a benchmark to evaluate the performance and computation time of the proposed heuristic, as well as solutions obtained through genetic algorithms and surrogate optimization. We conduct a simulation study using the proposed heuristic to analyze the impact of the scattering of stocks on order picking performance in a single block, low level, picker-to-parts warehouse. We also present the impact of warehouse operating parameters on the performance of the proposed scattered storage policy.