面向B2C电商订单的自动小车存取系统动态储位优化

Dynamic optimization of goods location in AVS/RS for B2C ecommerce order

为提高B2C电商订单拣选速度,分析单一订单和多个订单的情况下,品项在自动小车存取系统(AVS/RS)中的货位变化对系统出库作业时间的影响;提出基于节约时间的多层次启发式聚类算法,利用订单品项间的相似系数进行聚类;从商品价格折扣和被定频次两个角度提出品项物流价值模型,并以此为货位优化的依据;构建了仿真模型,对聚类方法及储位优化策略的有效性进行验证。结果表明,基于节约时间法的多层次启发式聚类算法效果较好,和传统的全周转率货位分配策略相比,考虑商品价格折扣的品项物流价值的动态储位优化策略可以明显提高订单出库效率。

In the case of single order and multiple orders,the impact of Autonomous Vehicle Storage and Retrieval System(AVS/RS)system operation efficiency by changing the item’s allocation was analyzed,and a time-saving multi-level heuristic clustering algorithm which used the similarity coefficient between order items to cluster was proposed.By combining the price discount for goods and order count,the model of item-logistics-value was proposed and used as the basis for the optimization of the item’s allocation.A simulation model was constructed to verify the effectiveness of the clustering method and the storage optimization strategy.The results showed that the multi-level heuristic clustering algorithm based on time-saving method had a good effect;the dynamic storage optimization strategy based on item-logistics-value had a significant effect on improving order delivery efficiency which had considered the price discount and order frequency by comparing with the traditional full turnover rate allocation strategy.