机器人履约系统任务分配与货架储位再指派联合优化

Joint Optimization for Automated Guided Vehicle Task Assignment and Pod Repositioning in Robotic Mobile Fulfillment System

为优化移动机器人履约系统(robotic mobile fulfillment system,RMFS)中移动机器人运行成本,提出一种考虑移动机器人任务分配和货架储位再指派的联合优化策略。以移动机器人完成任务成本最小为优化目标,构建考虑移动机器人重载和空载成本差异的数学模型,并用遗传算法对模型进行求解。经与返回原位置及返回距离拣选台最近位置两种策略进行仿真实验对比,结果表明联合优化策略可以有效降低移动机器人完成任务成本,提高拣选效率。

To optimize the operating cost of mobile robots in the robot mobile fulfillment system(RMFS),a joint optimization strategy was proposed that considers mobile robot task assignment and pod repositioning.A mathematical model was constructed,conside-ring the difference between the heavy load and the no-load cost of the mobile robot,with the goal of minimizing the cost of completing the task by the mobile robot.The model was solved using the genetic algorithm.Compared with the two strategies of returning to the original position and returning to the closest position to the picking station,the results show that the joint optimization strategy can effectively reduce the cost of completing tasks by mobile robots and improve picking efficiency.