基于混合元启发式算法的订单分批问题

订单拣选是仓库运营管理中一项高劳动强度与高成本的操作,拣货员在仓库中从货位拣选出满足订单需求的货物.订单分批问题(order batching problem, OBP)是订单拣选中的重要规划问题,该问题以最小化拣选批次路径时长为目标,将用户订单分配至拣选批次中.首先,为了优化订单分配构造高质量批次,提出一种混合元启发式算法,在自适应大邻域搜索框架中融入基于不可行下降的局部搜索,同时引入自适应惩罚机制和一批基于订单与基于批次的移除启发式以及新的算法组件;其次,为了优化拣选路径进一步降低批次旅行时间,提出单向启发式,利用动态规划优化组合多个路径策略.实验表明,在合理计算时间内,所提出算法的求解质量优于多重启变邻域搜索(MS-VNS)、混合自适应大邻域搜索及禁忌搜索(ALNS/TS),而且所提出算法的最大路径长度减少率达到22.36%.

Hybrid metaheuristic algorithm for order batching problem

Order picking is one of the most laborious and high-cost processes in the warehouse management and operation. Pickers retrieve goods from their storage locations in order to satisfy orders. The order batching problem(OBP) is a planning problem, which is critical for order picking. Customer orders are grouped into batches in such a way that the total travel distance of all pickers is minimized. First, in order to tackle the assignment of orders to obtain high quality batches, the hybrid metaheuristic algorithm is proposed, which incorporates an infeasible descent procedure into the adaptive large neighborhood search framework. An adaptive punishment mechanism, removal heuristics related to customer orders or batches and additional algorithmic components are also introduced. Second, to address the picker routing and hence reduce batch travel time, the unidirectional heuristic is proposed using a dynamic programming approach to combine routing heuristics. By means of computational experiments, the proposed algorithm is compared to the ALNS/TS and the MS-VNS. It is demonstrated that, in reasonable computing times, the algorithm provides solutions of excellent quality which lead to a reduction of the total travel distance by up to 22.36%.