基于改进自适应遗传算法求解机床制造企业立体仓库堆垛机路径优化问题

Solving the warehouse stacker routing problem of machine tool manufacturers based on adaptive genetic algorithm

以机床制造业为背景,以存放机床零部件的自动化立体仓库为基础,重点分析自动化立体仓库堆垛机的路径分析。通过对遗传算法进行自适应改进,算出能够随时适应的遗传算子,克服了传统遗传算法的早熟收敛问题。通过运用序号法设定各货位在立体仓库中的位置,建立堆垛机拣选作业的数学模型,运用改进自适应遗传算法对初始路径进行改进,得出最优解,并运用Matlab遗传算法工具箱对此进行仿真,实验结果表明,此方法收敛速度快,可以获得全局最优解,其堆垛机路径规划更加快速和有效。

Using machine tool manufacturing industry as the background,based on automated warehouse storing machine parts,the automated warehouse stacker path is analyzed emphatically.Through the improved adaptive genetic algorithm,genetic operators can be calculated at any time to adapt,overcome premature convergence of traditional genetic algorithms.Method to set the serial number through the use of cargo space in the location in the warehouse,picking stacker established mathematical model,using the improved genetic algorithm to improve the initial path,the optimal solution,and using Matlab Genetic Algorithms Box to simulate,experiment results show that this method converges faster and can get global optimal solution,the stacking machine path planning is more quick and efficient.