Study on the Selection of Empty Return Depot Location for Car Transport

This thesis focuses on vehicle logistics scheduling, aiming to improve transportation efficiency and reduce costs through demand forecasting and scheduling optimization. This paper reviews the domestic and foreign demand forecasting methods, points out the advantages of combined forecasting in improving accuracy, and puts forward a scheduling optimization model based on combined forecasting in view of the shortcomings of existing research on double-deck car scheduling and terminal location. By using Astar algorithm and integer linear programming (ILP), considering demand and distance comprehensively, the docking and no-load scheduling decisions are optimized, which solves the problem of traditional scheduling relying on experience. The effectiveness of the model is verified by an example analysis, which significantly reduces the transportation cost and improves the resource utilization rate. The research provides theoretical support and practical guidance for vehicle logistics scheduling, and the application of intelligent algorithm and environmental protection scheduling mode can be explored in the future.