Dynamic Vehicle Distribution Path Optimization Based on Collaboration of Cloud, Edge and End Devices

Abstract

Aiming at the problems of unreasonable distribution routes in the current logistics distribution field, without considering the impact of real-time road conditions, and the inability to reduce the impact on the timeliness of distribution, this paper proposes a dynamic vehicle distribution path optimization method based on the collaboration of cloud, edge and end devices. This method considers the requirements of demand points for the delivery time and considers the changes in road traffic conditions caused by random road traffic incidents. Combining the characteristics of vehicle speed and time penalty cost in the vehicle delivery process establishes a logistics delivery vehicle path optimization model. Solve it and optimize it with the A* algorithm and dynamic schedule. This method collects road condition data in real-time through terminal equipment, evaluates and judges road conditions at the edge, and makes real-time adjustments to the distribution plan made in advance at the cloud data center. Through simulation experiments on application examples, the vehicle path optimization method proposed in this paper that considers real-time road conditions changes and the optimization method that does not consider road conditions are compared and analyzed, verifying the effectiveness of this method. Experimental results show that this method can reduce distribution costs, reduce distribution time, and reduce the impact of changes in road conditions on the distribution results.