基于蚁群遗传算法的自动化立体仓库拣选路径优化

Order Picking Optimization of Automated Warehouses Based on the Ant Colony Genetic Algorithm

合理优化货物的拣选路径是提高自动化立体仓库运行效率的一种有效方法。通过分析自动化立体仓库拣选作业的工作流程与特点,为自动化仓库拣选作业建立优化数学模型,首先利用蚁群算法生成优异的初始种群,然后通过遗传算法对该数学模型进行优化求解。仿真结果表明该模型是可行的,蚁群遗传算法的混合不仅得到更精确的结果而且加速了算法的求解速度,从而能够改善拣选作业的效率。

Optimizing the order picking is a useful way to improve the efficiency of automated warehouses. According to analyzing the process and characteristics of picking in automated warehouses, a new mathematic model is proposed for automated warehouses. Firstly, we make an excellent initial population by the ant colony algorithm, and then optimize and solve the model with the genetic algorithms. The simulation results show that the model is feasible, and the mix of the ant colony and genetic algorithms is not only feasible but also accelerate the speed of the algorithm, and then improve the efficiency of order picking.