考虑订单拆分策略的AGV拣选效率优化方法

Optimization method of AGV picking efficiency considering order splitting strategy

为提高智能仓库系统中AGV的拣选效率,针对AGV订单拣选优化问题分为AGV-货架任务分配、多AGV无冲突路径规划两个子问题进行研究,根据订单特点引入订单拆分策略,并以最小化AGV完成所有订单的总时间为目标构建数学模型。首先,设计了确定货架优先级的AGV-货架任务分配算法(AGV-shelf task allocation algorithm, ASTA)求解匹配问题。然后,提出一种带有贪婪参数并嵌入冲突消解策略的改进Q-Learning算法,得到拆分策略下最优无冲突拣选路径方案。最后,通过在40 m×40 m仓库布局中的订单集数值实验对比分析,所提算法与现有的两种算法对比结果显示,AGV完成所有订单的总时间分别平均减少11.63%和26.74%,验证了拆分策略的有效性,并且通过AGV使用数量、完成订单时间和路径冲突等待时间占比三个指标的对比验证了拆分策略和所提算法能有效缓解拥堵情况,减少行驶路径长度,提高拣选效率。此外,针对AGV数量灵敏度分析,在不同数量的AGV对行驶时间和路径冲突等待时间的影响方面,发现19台AGV数量是最佳配置,验证了模型的可行性和算法的有效性。

To improve the picking efficiency of AGVs in intelligent warehouse systems,aiming at two sub-problems of AGV-shelf task allocation and multi-AGV conflict-free path planning,this paper introduced order splitting strategy according to the order characteristics and built a mathematical model with the objective of minimizing the total time for AGVs to complete all orders.Firstly,it designed the ASTA to solve the matching problem by determining the shelf priority.Secondly,it proposed an improved Q-Learning algorithm with a greedy parameter and embedded conflict elimination strategy to obtain the optimal conflict-free picking path scheme under the splitting strategy.Finally,through experimental comparative analysis of order set values in the 40 m×40 m warehouse layout,the proposed algorithm was compared with the two existing algorithms.The results indicate that the proposed algorithm reduced the total time for AGVs to complete the all orders by an average of 11.63%and 26.74%respectively,which verified the effectiveness of the splitting strategy.And it was verified that the splitting strategy and the proposed algorithm can effectively alleviate the congestion,reduce the length of traveling paths and improve the picking efficiency,through the comparison of three indexes of the number of AGVs used,the time of complete orders and the percentage of waiting time for path conflict.Furthermore,for the sensitivity on the number of AGVs,it tested the influence of different numbers of AGVs on the travel time and path conflict waiting time.It was found that the number of 19 AGVs is the optimal configuration.These results verify the feasibility of the model and the effectiveness of the proposed algorithm.