A Novel Fulfillment-Focused Simultaneous Assignment Method for Large-Scale Order Picking Optimization Problem in RMFS
针对机器人移动货架系统中大规模订单与货架同时分配难题，提出两阶段方法：先用混合自适应大邻域搜索压缩关键货架集，再基于边际收益同时分配订单与货架到拣选站，显著优于现有算法。
The emergence of a robotic mobile fulfillment system (RMFS) provides an automated solution for e-commerce warehousing to improve productivity and reduce labor costs. This article studies the order picking optimization problem in RMFS, which simultaneously decides the assignment of orders and racks to multiple picking stations. Although this problem has been widely studied in recent years, it is still very challenging for existing methods to solve large-scale instances effectively (e.g., more than 200 orders and 500 racks). To overcome this difficulty to meet the real-world needs, we propose a fulfillment-focused simultaneous assignment (FFSA) method. The proposed FFSA comprises two stages: 1) compression and 2) simultaneous assignment. The compression stage employs a hybrid adaptive large neighborhood search (ALNS) strategy to establish a reduced set of critical racks that can fulfill the demand of all orders. In the simultaneous assignment stage, we develop a marginal-return-based assignment with candidate strategy (MRACS) to simultaneously assign orders and critical racks to picking stations. MRACS takes into account three fulfillment-focused measurements to depict the product supply relationship between the demand of orders and the inventory on critical racks. These measurements are further integrated into the effective heuristics with sufficient problem-specific knowledge to obtain a high-quality solution. Experimental results show that our method significantly outperforms representative algorithms on both synthetic data and large-scale real-world data.