Optimization Analysis of Cold Chain Logistics Network Structure Based on Genetic Algorithm

This study addresses the challenging problem of path planning in intelligent warehousing and proposes an innovative AGV path optimization method. This method, based on the ant colony algorithm, introduces a dynamic pheromone update mechanism and an adaptive parameter adjustment strategy, significantly enhancing the algorithm's convergence speed and global search capability. In a 25×25 grid-based simulation environment, the improved algorithm achieves an optimal path length of 38 in just 60 iterations, compared to the basic ant colony algorithm's 73 iterations and path length of 40, demonstrating a marked increase in efficiency. Further large-scale scenario testing and simulations in dynamic obstacle environments have validated the algorithm’s scalability and adaptability, showcasing its outstanding performance in complex warehousing settings. This breakthrough not only enriches the theoretical framework of intelligent path planning but also provides a practical and effective technological solution for improving the efficiency of actual warehousing systems. The results of this study infuse new vitality into smart logistics, advancing warehousing automation towards greater efficiency and intelligence, and are expected to play a crucial role in the future of intelligent warehousing practices, paving the way for innovation in the industry.