Research on vehicle routing problem based on real-time vehicle speed prediction

Researches on vehicle routing for logistics enterprises often neglects the impact of vehicle speed on operational costs. In order to address this issue, a genetic algorithm is designed to optimize the BP neural network model for vehicle speed prediction, which utilizes big data to predict the vehicle speed on different segments of the distribution network. Taking into account the demand for goods at distribution outlets, a vehicle routing model is constructed. The decision variables of the model include the vehicle delivery routes and the corresponding vehicle types. The objective is to minimize the total cost of distribution for the enterprise. A case study validates the effectiveness of the model by using a distribution network with 21 distribution outlets as nodes. We implemented the genetic algorithm program in the Matlab software to solve the case study. The results demonstrate that the optimized solution can effectively reduce distribution costs for logistics enterprises.