面向FMS基于改进的混合PSO-GA的多AGV调度算法研究

Research on Muti-AGV Sechduling Algorithm Based on Improved Hybrid PSO-GA for FMS

在柔性制造系统(Flexible Manufacturing System,FMS)中,自动导引小车(Automated Guided Vehicle,AGV)常被用于搬运物料或产品,因此AGV的优化调度成为提高生产效率的关键。AGV的调度除了要考虑AGV的任务分配问题,还需要参考每个操作的花费时间、小车的运行时间等因素。相比于单AGV调度算法,多AGV多任务调度算法需要一个更加复杂的模型来支撑。在考虑AGV的电量状况下,以最小完成时间与调度最少AGV数量作为优化目标,提出了一种改进的混合遗传算法与粒子群算法(PSO-GA),并基于该算法给出了多AGV调度模型,在此基础上进行了仿真实验。结果表明,相较于单一的GA或PSO算法,所提算法在全局寻优收敛与运行时间上有明显的优化效果,而相比于现有的混合PSO-GA算法,其在搜索精度和收敛速度上有进一步提高。

The antomated guided vehicle(AGV)is often used to transport materials for improving prodiction efficiency in manufacturing facility or a warehouse.AGV scheduling not only needs to consider the AGV task assignment problem,but also needs to consider the time spent for each operation and the running time of the car.Compared with single-objective optimization scheduling algorithm,multi-objective optimization requires a more complex model to support.This model optimizes the two dimensions of minimizing the completion time and scheduling the minimum number of AGVs considering the power status of the AGV.This paper presented an improved hybrid particle swarm optimization and genetic algorithm(PSO-GA)to optimize the model.Compared with the GA or PSO algorithm,the proposed algorithm has significant optimization effect.Compared to PSO-GA hybrid algorithm,it is further improved in the running time.