Optimization of Logistics Vehicle Path Planning Model Based on Improved Ant Colony Algorithm and "Hitchhiking" Distribution Mode

In the logistics and transportation link, vehicle scheduling determines its speed, cost and benefit to a great extent. Therefore, optimizing vehicle scheduling is the key to optimizing the transportation link. This paper draws on the concept of "hitchhiking" in the car-hailing industry to propose a "hitchhiking" distribution model, that is, when different paths have a large number of repeated distribution points, the goods are transferred in the cargo workshop, and a new method based on improved ant colony algorithm and A dynamic vehicle scheduling optimization method for multi-model and multi-distribution centers in the "hitchhiking" distribution mode. The method firstly constructs a traffic flow prediction model considering energy consumption to provide road condition information for path planning; based on the basic ant colony algorithm, the energy consumption model is used to improve the heuristic factor, and a new energy-saving ant colony algorithm is proposed combined with the path segmentation algorithm. Energy-saving Ant Colony Optimization combines the optimized "hitchhiking" distribution mode for route planning. At the same time, this paper is verified by the path planning simulation sample analysis. The results show that the vehicle scheduling results under the optimization model are more reasonable and have obvious low-carbon and energy-saving benefits.