Research on the Application of Artificial Intelligence Technology in Logistics Route Optimization

Abstract

The global trade environment has become complex and the demand for end-point distribution has significantly increased. The traditional logistics scheduling model cannot handle large amounts of data or adapt to changes in the dynamic environment. This paper analyzes the application methods of artificial intelligence technology in the vehicle routing problem VRP. By analyzing the internal mechanisms of deep reinforcement learning, graph neural networks, and hybrid heuristic algorithms, a model for multi-objective dynamic optimization was formed. The analysis shows that by accurately capturing spatio-temporal features, AI technology will be effective in reducing distribution costs and shortening the time taken to make decisions. Experimental results show that this solution is more efficient than traditional algorithms in handling problems under complex constraints, providing a theoretical basis and practical methods for the transformation of intelligent logistics.