Research on E-commerce Logistics Transportation Route Planning Method Based on Recurrent Neural Network

Aiming at the problem that the traditional transportation route planning method can adaptively allocate a small number of transportation points, which leads to the long time required for the final route planning, a recurrent neural network-based e-commerce logistics transportation route planning method is studied. Use recursive neural network to extract e-commerce logistics characteristics, calculate the adaptive probability parameters shown by individual transportation individuals, combine gene blocks to control adaptively processed transportation points, set transportation route neighborhood search mechanisms, and build transportation route cost constraint numerical relationships. Combining the congestion parameters generated when the vehicle is running, set the route planning plan, and finally complete the research on the transportation route planning method. After preparing the e-commerce logistics transportation data, simulate the planning of the e-commerce logistics transportation route, apply two traditional transportation route planning methods and the designed route planning method to experiment, and the results show: the planning time required for the designed route planning method The shortest.