“RMFS”拣选系统的订单分批优化

Order batch optimization of″RMFS″picking system

在全球零售额和当天交货量不断增长的时代,实现订单的快速交付和优质分批是影响移动机器人履行系统(Robotic Mobile Fulfillment Systems,RMFS)拣选效率的关键因素.为构造高质量订单分配批次、提升RMFS系统拣选效率,提出融合大邻域搜索的改进差分进化算法(LNS_DE),引入大邻域搜索的破坏与修复思想及一批基于随机、基于最大代价贡献和基于集中批次的移除算子以及新的插入算子组件,以最小化订单总延迟时间为目标建立订单分批优化模型,并针对不同订单规模算例进行实验仿真.仿真结果表明,所提出的订单分批优化算法较差分进化算法(DE)相比求解质量更优,性能更稳定、收敛速度更快,尤其当订单数量增大时,LNS_DE算法解的平均值优化比例不断扩大,这为提高RMFS系统拣选效率,实现订单快速响应提供有效决策指导.

In an era of increasing global retail sales and same-day delivery,fast delivery and quality batching of orders are the key factors affecting the picking efficiency of RMFS system.In order to construct high-quality order allocation batches and improve the order-picking efficiency of RMFS,an improved differential evolution algorithm(LNS_DE)is proposed,which integrates the idea of destruction and repair of large neighborhood search,a batch of removal operators based on random,maximum cost contribution and centralized batches,and a new insertion operator component.The order batching optimization model is established with the objective of minimizing the total delay time of orders,and the simulation experiments are carried out for different order sizes.The simulation results show that the proposed order batching optimization algorithm has better solution quality,more stable performance and faster convergence speed than the differential evolution algorithm.Especially when the number of orders increases,the average optimization proportion of the LNS_DE algorithm solution expands,which provides effective decision guidance for improving the order-picking efficiency of RMFS system and achieving rapid response to orders.