How to benefit from order data: correlated dispersed storage assignment in robotic warehouses
研究了在机器人仓库中利用订单数据优化存储分配的方法，通过考虑产品周转频率、相关性和分散策略，提出混合整数线性规划模型和启发式算法，减少机器人拣选时间，对电商仓储管理者有参考价值。
In e-commerce fulfilment centres, storage assignment is critical to ensure short response times. To achieve this, many online retailers have moved to product dispersion in combination with product turnover-based slotting. However, commonly used policies do not fully utilise the historical customer demand information to optimise the storage assignment. This paper addresses a comprehensive approach to estimate the joint effects of ‘turnover frequency’, ‘product correlation’, and ‘inventory dispersion’ storage strategies on the expected order picking travel time in automated (robotic), parts-to-picker systems. Additionally, it provides a thorough analysis of the impact of product correlation and turnover frequency on storage policies’ performance. We develop a mixed-integer linear program for optimal product-to-cluster and cluster-to-zone allocation to minimise the robot's expected travel time. The travel time expressions are developed for different zone and station configurations. An efficient construction and improvement heuristic method is proposed and applied to a real dataset of a personal care products distributor. The analytical results show that the correlated dispersed assignment leads to a shorter expected travel time than the benchmark policies for order sets with sufficiently large order size. The demand correlation plays a major role in the performance of the models in the cases we tested.