Bayesian Optimization for Adaptive Network Reconfiguration in Urban Delivery Systems

Urban delivery systems face numerous challenges due to fluctuating demand and dynamic traffic conditions, necessitating efficient route management for improved performance. We introduce a Bayesian Optimization framework designed for adaptive network reconfiguration in such environments. The method leverages Bayesian inference to model complex interactions within urban settings, allowing for real-time updates to delivery network configurations as conditions change. By integrating real-time data, this framework optimizes delivery routes, enhancing both efficiency and reliability. Experiments conducted in simulated urban delivery scenarios demonstrate notable advancements in performance metrics, highlighting improved time efficiency and resource utilization. The study showcases the potential of Bayesian optimization in making informed, effective decisions in real-world delivery logistics, paving the way for progress in smart urban transport systems.