Research and Implementation of Logistics Distribution System Based on Ant Colony Algorithm

In the context of global integration, the efficiency and cost-effectiveness of logistics and distribution systems are crucial to improving corporate competitiveness. In view of the problems such as high cost, low efficiency and slow response speed in urban logistics distribution, this paper uses the ant colony optimization (ACO) algorithm to construct a road network optimization model for intelligent road planning. The research first analyzes the application status of ant colony algorithm in the field of logistics and distribution and the challenges it faces, and improves the search efficiency and adaptability of the algorithm through parameter optimization and heuristic strategy adjustment. Experimental results show that compared with genetic algorithm, simulated annealing algorithm and nearest neighbor algorithm, ant colony algorithm has significant advantages in average delivery time, vehicle utilization and average delivery cost. The research results of this article not only highlight the application potential of the ant colony algorithm in actual logistics distribution, but also provide a new technical approach for the optimization of logistics distribution systems.