Optimization of Circular-Rail RGV Scheduling in a Tobacco Warehouse

Continuous operation in tobacco warehouses places high requirements on circular-rail rail guided vehicle (RGV) dispatching, because task concentration, track interference, and equipment abnormalities may quickly cause vehicle waiting and material-flow delay. To improve the response capability of this system, this study develops a hybrid scheduling method based on an improved genetic algorithm and simulated annealing. According to the operating characteristics of the circular rail, a scheduling model is formulated to minimize the overall task completion time while considering rail operation rules and composite task requirements. In the solution process, the simulated annealing temperature is used to adjust parent selection and mutation behavior, so that the algorithm can maintain search diversity in the early stage and improve convergence in the later stage. Case results show that the proposed method reduces the average maximum completion time from 150.2 s to 143.4 s and improves the best result from 145.7 s to 139.6 s compared with the conventional genetic algorithm. These results indicate that the proposed method can improve RGV response efficiency and provide decision support for stable warehouse logistics under disturbances such as task fluctuation and path conflict.