Transportation vehicle scheduling optimization method based on improved multi-layer coding genetic algorithm

In order to improve the capacity of transportation vehicles scheduling in complex geographical structure cities, an optimized scheduling algorithm for transportation vehicles in complex geographical structure cities based on improved multi-layer coding genetic algorithm is proposed. The map grid model of urban transportation vehicle route planning with complex geographical structure is constructed, and the clustering between urban road traffic target points and information difference degree are used for fusion clustering analysis, so as to construct an artificial routing optimization control model of urban road traffic planning vehicles, and realize the update of the optimal scheduling of urban road traffic planning vehicles by combining the reference factors of environment, map and pheromone. Get the instance initialization parameter distribution model of urban road traffic planning vehicles, optimize the spatial configuration parameters of urban traffic vehicles with complex geographical structure, build a hybrid improved multi-layer coding genetic evolution optimization model for optimal scheduling of urban traffic vehicles with complex geographical structure, and build an artificial intelligence algorithm for optimal scheduling of urban traffic vehicles with complex geographical structure according to the hybrid improved multi-layer coding genetic path constraint optimization method. The simulation results show that this method has better optimization ability and stronger path planning ability for urban transportation vehicle scheduling with complex geographical structure, and improves the efficiency of vehicle scheduling.