基于改进遗传算法的货箱机器人拣选路径规划

Picking Path Planning of Container Robots Based on Improved Genetic Algorithm

针对智能仓库中新型“货箱到人”拣选模式下多个货箱机器人拣选路径规划问题,给出了一种新的优化模型和改进遗传算法。基于货箱机器人的拣选方式及特点,将其转化为非对称车辆路径问题,以机器人总拣选路径最短和完成时间最少为双目标建立混合整数规划模型,设计改进的混合遗传算法对模型进行求解,并通过大规模算例验证了算法的有效性与稳定性。算例计算结果表明:所建模型及算法提高了货箱机器人的拣选效率,降低了运行成本。

Under the new"container-to-person"picking mode in intelligent warehouses,a new optimization model and its improved genetic algorithm are proposed to solve the picking path planning problem of multiple container robots.According to the picking mode and characteristics of container robots,the picking path planning problem is transformed into an asymmetric vehicle routing problem,and a mixed integer programming model is established with bi-objectives of the shortest total picking path and the least completion time.A hybrid genetic algorithm is designed to solve this model,and the effectiveness and stability of the algorithm are verified through large-scale examples.The computational results demonstrate that the picking efficiency of container robots is improved by the proposed model and its algorithm,and their total picking cost is reduced.