Dynamic Path Planning Models and Algorithms for Urban Last-Mile Logistics Distribution

초록

Urban last-mile logistics distribution faces significant challenges due to dynamic factors such as real-time traffic conditions, order fluctuations, and delivery constraints. This paper presents a comprehensive study on dynamic path planning models and algorithms tailored for urban last-mile logistics. A multi-objective mathematical model is developed to minimize delivery time, cost, and environmental impact while considering vehicle capacity, time windows, and real-time data integration. Three advanced algorithms-Dynamic Dijkstra with Real-Time Weight Adjustment (DDRWA), Reinforcement Learning-Enhanced Ant Colony Optimization (RL-ACO), and Multi-Objective Particle Swarm Optimization (MOPSO)-are proposed and evaluated. Experimental results using the Solomon benchmark dataset and real-world logistics data demonstrate that the proposed algorithms outperform traditional methods in terms of path efficiency and adaptability to dynamic changes. © 2025 IEEE

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