Dynamic scheduling of intelligent Rail Guided Vehicle based on queuing theory

In the automated warehouse, the conveying system connects multi-layer shelves and external material delivery tools, and its efficiency is very important for the entire automated warehouse. In order to improve the efficiency of Guided Vehicle, the dynamic scheduling problem of Rail-Guided Vehicle (RGV) in the Guided Vehicle is investigated. Firstly, the key problems in dynamic scheduling of RGV are analyzed. On this basis, eM-plant is used to model the entire automated three-dimensional warehouse, so as to make further research on dynamic scheduling of RGV by means of simulation technology. Secondly, shortest path generation, queuing theory and genetic algorithm are used to solve the model. Finally, according to different situations and different Computer Number controllers (CNC) operation time, the scheduling mode of RGV is given. The research results show that both the basic genetic algorithm and the improved genetic algorithm can be used to solve the model of large material flow, and an optimized scheduling scheme of multiple RGV can be obtained to improve the efficiency of the transportation system. However, the improved genetic algorithm is more effective and can greatly improve the efficiency of the transportation system.