Storage Allocation of Perishable Products in Warehouses

Abstract

The growing complexity of e-commerce fulfillment has amplified the importance of storage location assignment (SLA) in warehouses, where order-picking is the most resource-intensive activity. While turnover-based, correlated, and scattered storage strategies are well-established, existing approaches typically rely on adopting one strategy in isolation or combine them by using subjective parameter tuning, limiting their adaptability to dynamic operating contexts. For the first part of this PhD, we introduce a data-driven framework that balances these basic SLA strategies according to customer order patterns. First, novel analytical measures are developed to quantify the degree to which each basic SLA principle is realized. Second, a weighting scheme derived from historical order data is proposed to enable context-specific adaptation without decision-maker intervention. These measures and dynamic weights are then integrated into a new multi-objective mathematical model that assigns items to storage locations, taking existing inventory into account. The model is validated in an autonomous mobile robot–assisted order picking system using a factorial experiment across multiple operating contexts. Results demonstrate that the proposed balanced SLA approach significantly outperforms basic strategies by reducing picker travel distance and mitigating order-line splitting. For the second part, we aim to enrich our study on SLA with a specific business context, that is the SLA of fruits and vegetables in a retail setting. This selection is based on three reasons: (a) retail and consumption have the largest share of food waste; (b) food retailers are the largest commercial users of refrigeration, accounting for 30% of the food sector’s electricity consumption; and (c) lower storage temperatures often extend shelf life but at the expense of higher energy consumption. Therefore, our goal is to propose an SLA approach that not only reduces food loss and energy consumption in a retailer’s warehouse, but also mitigates food loss further along the supply chain.