On-Demand Urban Air Mobility Scheduling with Operational Considerations

This paper introduces an on-demand sequencing and scheduling framework for Urban Air Mobility (UAM) with electric vertical takeoff and landing (eVTOL) aircraft. Safety and efficiency, considering factors such as battery state of charge and charging infrastructure, are critical factors for UAM operations. A new scheduling framework integrating considerations for battery consumption, parking and charging infrastructure, vertiport throughput, and fleet heterogeneity to maximize the operational efficiency of the eVTOL UAM fleet is proposed. A solution methodology utilizing a genetic algorithm and receding horizon scheduling achieves near-optimal solutions with an average optimality gap of 5.8% and a runtime of less than 1 min for dynamic scheduling. A case study based on the 2024 Paris Olympic air taxi operations demonstrates the efficacy of the proposed problem formulation and solution method.