Optimization of last-mile delivery using dynamic path algorithms based on reinforcement learning

Abstract

Deep reinforcement learning has emerged as a transformative approach for solving the challenges of urban last-mile delivery, characterized by rapidly changing demand and unpredictable traffic conditions. The study uses a dynamic routing framework to create adaptive routing strategies, centered on a customized deep Q-network. By collecting real-time traffic data, vehicle status and delivery priority, the framework can formulate routing strategies. Using advanced data fusion algorithms, the method integrates heterogeneous sensors and traffic inputs. It also creates a composite reward function to balance operational costs, on-time delivery, and service quality. Simulations in a high-fidelity urban environment show that the proposed system can reduce the total delivery cost by 15 to 20 percent compared to static routing methods, while improving energy utilization and time efficiency. Sensitivity analysis shows that the method works well in variable traffic scenarios and effectively adapts to peak and off-peak delivery windows. These findings suggest that deep reinforcement learning and real-time data integration, along with multiobjective policy design, are effective solutions for optimizing modern urban logistics and last-mile delivery networks.