arXiv:2412.09978v5 Announce Type: replace-cross Abstract: In this study, we develop a dynamic charging scheduling method that integrates day-ahead charging planning and online reactive vehicle dispatch and charger assignment. The day-ahead planning model minimizes expected charging costs by considering anticipated waiting times and overnight energy replenishment costs. The online model then adapts this plan by maximizing the profit from vehicle dispatching and assigning vehicles to chargers through a reservation-based charging request scheduler to minimize the total charging-related operational time. The developed charging policy is tested on a Manhattan-like study area using synthetic data drawn from NYC yellow taxi data with a fleet size of 100 vehicles given the scenarios of 3000 and 4000 customers/day. The computational results show that our method outperforms different benchmark policies with up to +21.68% profit and +22.22% service rate for 4000 customers relative to the weakest benchmark. Sensitivity analyses are conducted for different system parameters, and managerial insights are discussed.