Logistics distribution vehicle scheduling method based on improved A* algorithm

Abstract

The invention relates to a logistics distribution vehicle scheduling method based on an improved A* algorithm, and proposes a plan for vehicle scheduling, route selection and route planning in logistics distribution including two parts: an improved A* algorithm and a logistics distribution algorithm. The improved A* algorithm is used to quickly search for the optimal path between two points, including: meshing the distribution area, preferably selecting the appropriate evaluation distance, and recursively searching for the shortest distance. The logistics distribution algorithm is used to generate vehicle information sent from the distribution center to each customer node, the vehicle information includes vehicle numbers, passing customer nodes, vehicle routes, load capacities, and total distances of the routes, and the logistics distribution algorithm includes two parts of calculating distances and routes from the customer nodes to the logistics center, and generating the distributionplane with the improved weighted graph algorithm. This scheduling method considers the constraints in the vehicle path, reduces the total transportation distance, enhances the overall control and management of the logistics distribution process, and achieves a more economical distribution line.

Description

technical field [0001] The invention relates to the technical field of the Internet of Vehicles, in particular to an improved A * Algorithmic logistics distribution vehicle scheduling method. Background technique [0002] The Vehicle Scheduling Problem (VRP) in the logistics distribution route is a typical combinatorial optimization problem, and it has been proved to be an NP-hard problem. It is difficult to solve the problem in a limited time with an accurate algorithm. [0003] Commonly used algorithms include Dijkstra algorithm and best-first search (BFS) algorithm, but both algorithms have certain shortcomings. Dijkstra is a typical single-source shortest path algorithm. When encountering U-shaped obstacles in the map, Dijkstra algorithm It will run slower, and the execution efficiency needs to be improved; although BFS is much faster than the Dijkstra algorithm, because it only considers the cost of reaching the target, it will not avoid it when it encounters U-shaped ...

AI Technical Summary

Problems solved by technology

[0002] The Vehicle Scheduling Problem (VRP) in the logistics distribution route is a typical combinatorial optimization problem, and it has been proved to be an NP-hard problem. It is difficult to solve the problem in a limited time with an accurate algorithm.

[0003] Commonly used algorithms include Dijkstra algorithm and best-first search (BFS) algorithm, but both algorithms have certain shortcomings. Dijkstra is a typical single-source shortest path algorithm. When encountering U-shaped obstacles in the map, Dijkstra algorithm It will run slower, and the execution efficiency needs to be improved; although BFS is much faster than the Dijkstra algorithm, because it only considers the cost of reaching the target, it will not avoid it when it encounters U-shaped obstacles in the map. choose a long path

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