Abstract: In the person-to-goods order picking system, after the customer places the order, the picker shuttles the warehouse to pick it. This paper studies the online order batching optimization problem under the condition that the capacity of the warehouse’s picking equipment and the number of picking personnel are limited, so as to prevent premature order or delayed service to complete the picking task in the shortest time. This paper constructs an online order batch planning model considering the minimum picking path to minimize the average effective order service time, and proposes a rule-based heuristic algorithm to solve the model, which includes [k]-means clustering algorithm and genetic algorithm to process separately orders planning of batching and picking paths. Finally, a specific example is used for simulation calculation. The experimental results show that compared with the traditional fixed time window heuristic algorithm, the proposed rule-based heuristic algorithm can significantly improve the efficiency of picking.

Key words: online order picking, order batching, picking path, clustering algorithm, genetic algorithm

摘要： 在人到货订单拣选系统中，客户下达订单后将由拣货员穿梭仓库进行拣选。在仓库的拣选设备容量和拣货人员数量有限制的条件下，研究在线订单分批优化问题，预防订单过早或延迟服务，以最短的时间完成拣货任务。构建考虑最小拣货路径的在线订单分批规划模型，以最小化平均有效订单服务时间。提出一种基于规则的启发式算法来求解模型，其中包含[k]-means聚类算法和遗传算法，分别处理订单的分批和拣选路径的规划。最后利用具体算例进行模拟计算，实验结果表明，与传统固定时间窗启发式算法相比，提出的基于规则的启发式算法能够显著提高拣货效率。

关键词: 在线订单拣选, 订单分批, 拣选路径, 聚类算法, 遗传算法