基于优化粒子群算法对智能RGV动态调度的研究

Research on intelligent RGV dynamic scheduling based on optimized particle swarm optimization

针对单轨直形穿梭车RGV(Rail Guided Vehicle)的动态调度策略,以单位周期8 h内加工数目最多为目标,分别对一道、两道工序及故障系统进行仿真模拟分析。在此基础上,运用粒子群优化算法、最短路径TSP(Traveling Salesman Problem)遗传算法对正常工作情况下一道、两道工序加工系统建立调度方案,同时利用概率函数法在故障情况下建立可修排队系统,最后利用MATLAB进行故障仿真,得到3组参数在不同故障率的系统作业效率,从而为RGV动态调度提供了最优CNC(Computer numerical control)加工循环序列。

Aiming at the dynamic scheduling strategy of single rail straight shuttle car RGV, the simulation analysis of one or two processes and fault systems was carried out with the aim of maximizing the number of processing in 8 h per unit cycle. On this basis, the particle swarm optimization algorithm and the shortest path TSP genetic algorithm are used to set up a scheduling scheme for one or two process processing systems under normal working conditions, while the probability function method is used to set up repairable queuing systems under fault conditions. Finally, the system operation efficiency of three sets of parameters at different fault rates is obtained by using MATLAB, thus providing an optimal CNC processing cycle sequence for RGV dynamic scheduling.