Optimization method for logistics distribution path of unmanned vehicles under vehicle road collaboration

This study aims to optimize the logistics distribution path of unmanned vehicles under vehicle road collaboration. The vehicle road resistance was calculated, and an optimization objective function for the logistics distribution path of unmanned vehicles was constructed based on the calculation results. The paper uses genetic algorithm to improve the ant colony algorithm, and the combination of the two can accelerate the convergence speed of the algorithm and improve the solving efficiency. Meanwhile, the population diversity of genetic algorithms and the pheromone propagation utilized by ant colony algorithms can also increase path diversity, making optimization results more robust and diverse. The improved ant colony algorithm was employed to optimize the delivery path. Experimental validation demonstrated that the proposed method outperforms others in terms of total delivery delay time and total distance of unmanned vehicle logistics delivery paths, offering higher efficiency and more optimized delivery plans. Specifically, the experimental results revealed that the total delivery delay time of this method is the shortest, not exceeding 30 minutes, and the shortest delivery path is only 100.3 kilometers.