基于改进遗传算法的印刷车间多AGV调度研究

Research on Scheduling of Multiple AGVs in Printing Workshop Based on Improved Genetic Algorithm

针对当前印刷车间内印刷品依赖人工搬运、AGV利用率低且多AGV作业时会发生碰撞、死锁等问题,本研究提出了一种改进的遗传算法对AGV的任务分配进行优化。首先,结合印刷车间的实际环境,以AGV搬运任务总时间最少作为目标函数进行建模;其次,利用加入了时间窗模型的改进双向A*算法对传统遗传算法进行优化,实现AGV的路径规划与调度管理;最后,利用MATLAB仿真软件对该算法进行仿真验证。对比传统遗传算法与改进遗传算法仿真结果,发现改进后的遗传算法得到的AGV调度结果搬运任务用时比传统遗传算法节省了13%,迭代次数比传统遗传算法减少了42%,因此改进后的遗传算法能更好的实现印刷车间内多AGV的无碰撞运行、路径规划与调度。

Aiming at the problems of manual handling of printed materials in the current printing workshop,low utilization rate of AGVs,and collisions and deadlocks in multi-AGV operations,an improved genetic algorithm was proposed to optimize the task assignment of AGVs.Firstly,combined with the actual environment of the printing workshop,the minimum total time of the AGV handling task was used as the objective function for modeling.Secondly,the traditional genetic algorithm was optimized by the improved bidirectional A*algorithm with the time window model added,which has realized the path planning and scheduling management of AGV.Finally,the algorithm was simulated and verified by MATLAB simulation software.Comparing the simulation results of the traditional genetic algorithm and the improved genetic algorithm,it had been found that the improved genetic algorithm yields a better AGV scheduling result,where the moving time for the transportation tasks was 13%shorter than that of the traditional genetic algorithm,and the number of iterations was 42%fewer than that of the traditional genetic algorithm.Therefore,the improved genetic algorithm can better achieve collision-free operation,path planning,and scheduling for multiple AGVs in a printing workshop.