Order batching problems in parallel-aisle order picking systems with larger-than-bin orders
研究了订单体积超过车辆箱容量时的装箱与分批问题，提出混合整数规划模型和启发式算法，在并行通道系统中实现近优解，适用于大规模订单场景。
Order batching facilitates order picking by merging orders into single vehicle trips. Filling orders with a total volume larger than a vehicle’s bin capacity, however, requires binning into multiple suborders, a procedure that influences batching performance by altering both the number of suborders and the routes for each suborder. This paper introduces the binning and batching problem (BBP) in an order picking system with pick support vehicles. To minimize the weighted sum of the number of bins and the total travel distance, we propose a binning and batching model (BBM) based on a mixed-integer programming (MIP) and an MIP-based heuristic for large-scale BBPs. Our heuristic obtains near-optimal solutions by the tight lower bound in the problems. A comparison of the heuristic and lower bound shows optimal gaps between 1.38 and 9.21% in a parallel-aisle system for 250–1000 orders. We demonstrate that the heuristic achieves the shortest travel distance for a large number of orders when the number of bins varies within a reasonable range.