Application of Intelligent Algorithm in Logistics Distribution Path Planning Under Statistical Model Optimization

In order to solve the problems of low efficiency and long distance in logistics distribution route planning, this paper collects and analyzes multi-dimensional information such as historical sales data, order demand, traffic conditions, etc., and uses time series analysis in statistics to establish a prediction model to accurately predict future distribution needs. The shortest path algorithm Dijkstra is used to construct a directed graph of the distribution area, and the optimal distribution route is calculated based on the distance between nodes and real-time traffic conditions. At the same time, a genetic algorithm is introduced for global search. By simulating the natural evolution process, the delivery path is continuously optimized to find the optimal solution that meets multiple objectives (lowest cost, least time). Compared with the algorithm before optimization, the length of the delivery path is shortened by about 43% on average, and the delivery time is reduced. The longest delivery time is shortened from 33.5 min to 19.8 min, and the shortest delivery time is reduced from 23.6 min to 10.3 min. In case studies during the Double Eleven peak period and under heavy rain, the delivery efficiency after algorithm optimization is improved by about 30% compared with traditional methods, and the emergency re-planning time is shortened by about 50%, providing a new solution for the logistics and distribution industry.