Abstract:

Based on the flow-rack zone order-picking system, it optimizes the flow-rack zone order-picking systems considering order batching methods and storage location assignment strategies. It considers reducing the residence time of the tasks and the waiting time of the pickers by reducing the working time differences between adjacent picking areas. A model is formulated whose objectives are minimizing the quantity of order batching and minimizing the sum of the differences of working time between any adjacent zones. And the model is solved by genetic algorithm to gain the order batching results and task release sequence. Considering storing the goods with higher turnover frequency into the layer of flow-rack where the items are the easiest to get, it proposes a classified random assignment strategy. The example shows that the results of the model perform well in the total completion time, total residence time, average fulfillment time and total waiting time compared to others. The classified random assignment strategy fundamentally reduces the picking time, which shortens the average fulfillment time and total completion time of the task, thereby improves the system efficiency and order response speed.

Key words: flow rack, zone order-picking, order batching, genetic algorithm, storage location assignment

摘要：

以流利式货架分区拣选系统为背景，考虑减小相邻拣选区域的作业时间差值以减少任务在缓存区中的滞留时间和拣货员在系统中的等待时间。建立以最小化订单分批数量和最小化所有相邻区域作业时间差值之和为目标的数学模型，设计遗传算法求解其订单分批结果及任务释放顺序；从拣货员拣选的便捷性出发，考虑将周转频率较高的货品存放至拣货员最易拣取的层数，提出了分类随机指派方式。案例分析结果表明：该模型结果在所有任务的总完成时间、总滞留时间、平均履行周期和所有拣货员在系统中的总等待时间等指标上均表现良好；分类随机指派方式从根本上减少了总拣货时间，进而缩短了任务的平均履行周期和总完成时间，提高了系统处理效率和订单响应速度。

关键词: 流利式货架, 分区拣选, 订单分批, 遗传算法, 储位指派