A congestion-aware vehicle routing optimization model for sustainable urban supply: An AnyLogic simulation approach

Urban traffic congestion significantly affects the efficiency, cost, and environmental performance of logistics operations. In particular, the selection of distribution routes plays a crucial role in ensuring timely delivery, reducing carbon emissions, and supporting sustainable urban mobility. This study proposes a congestion-aware supply path selection model that integrates congestion probabilities into the classical Dijkstra algorithm. By incorporating dynamic traffic conditions and assigning congestion probabilities to road segments, the model provides a more realistic representation of urban transportation networks. The AnyLogic simulation platform is employed to develop and validate the model, using the supply network of Wu-Mart supermarkets in Beijing as a case study. Simulation results demonstrate that the enhanced model effectively avoids congested areas, shortens transportation time, improves service efficiency, and reduces environmental impacts compared with traditional approaches. The findings highlight the feasibility and practicality of introducing congestion probabilities into urban vehicle routing problems, offering methodological support for logistics enterprises to optimize path planning. Moreover, this study contributes to the growing field of green logistics by demonstrating how congestion-aware routing can reduce fuel consumption and carbon emissions while maintaining delivery quality and efficiency. Future research should extend the model to multi-vehicle and multi-distribution center contexts, incorporating economic and environmental costs to further enhance sustainable logistics practices.