基于遗传算法的RGV动态调度研究

在巷道式自动化立体仓库中,环形轨道式导引小车系统(RGV)的效率是瓶颈.针对大物流量下RGV的动态调度问题,提出分组运输的方法,运用遗传算法把任务分配给各RGV,并针对具体问题提出了实用的编码方法.通过物流仿真软件eM-Plant比较了分组方法和先来先服务(First-come-first-serve)方法的效果,验证了模型的有效性.最后研究了RGV数量、出入货站台的数量以及每组任务数对产出量的影响,得到了相关的仿真数据,为环形轨道式导引小车系统的实际规划建设提供了可靠的依据.

Research on RGV dynamic scheduling based on genetic algorithm

As for narrow aisle automated storage/retrieval system, the bottle neck is the efficiency of rail guided vehi- cle （RGV） . Aiming at the RGV dynamic scheduling under large material flow, grouping transportation is proposed in the paper. By genetic algorithm any task is distributed to each RGV, and practical encoding method is also proposed for specif- ic problems. By the logistics simulation software eM-Plant, the effects of grouping method and first-come-first-serve method are compared to verify the effectiveness of the model. Finally, the effect of RGV quantity, quantity of stations and the number of tasks in each group on output is studied to get relevant simulation data, so as to provide reliable reference for the actual planning and construction of annular rail guided vehicles.