货箱到人模式下的AMR拣选路径规划  

Picking Path Planning for AMR under Container to Person Mode

作　　者：俞明哲 董宝力[1] 张书亭 YU Mingzhe;DONG Baoli;ZHANG Shuting(Faculty of Mechanical Engineering&Automation,Zhejiang Sci-Tech University,Hangzhou 310018,China)

机构地区：[1]浙江理工大学机械与自动控制学院,浙江杭州310018

出　　处：《物流科技》2022年第13期16-22,共7页Logistics Sci-Tech

基　　金：浙江省自然科学基金项目(LY16F020024)。

摘　　要：针对货箱到人拣选模式下AMR的拣选路径规划问题,构建以总作业时间最小化为目标的AMR拣选作业优化模型,并采用粒子群算法对模型进行求解。为解决标准粒子群算法存在易早熟收敛和陷入局部最优解等问题,设置二阶振荡环节和随机惯性权重,提出一种改进的粒子群算法。在实验分析中,将文章方法应用于某配送中心AMR库系统,并将改进的粒子群算法与标准粒子群算法进行比较。实例实证表明改进的粒子群算法具有更高的迭代效率和求解精度,相较于标准粒子群算法节约了15.6%的拣选时间,能有效降低货箱到人模式下AMR拣选作业耗时。Aiming at the picking path planning problem of AMR under container to person mode, an AMR picking optimization model was established to minimize the total operating time, and the particle swarm optimization algorithm is used to solve the model. In order to solve the problems of premature convergence and falling into local optimal solution of standard particle swarm optimization algorithm, an improved particle swarm optimization algorithm is proposed by setting the second-order oscillation link and random inertia weight. In the experimental analysis, this method is applied to the AMR warehouse system of a distribution center, and the improved particle swarm optimization algorithm is compared with the standard particle swarm optimization algorithm. The example shows that the improved particle swarm optimization algorithm has higher iterative efficiency and solution accuracy, and compared with the standard particle swarm optimization algorithm, the picking time is saved by 15.6%, can effectively reduce the time-consuming of AMR picking operation under container to person mode.

关 键 词：货箱到人 粒子群算法 路径规划 AMR 拣选作业 

分 类 号：F253[经济管理—国民经济]