Rolling horizon optimization of urban air mobility (UAM) service with shared riding, vertiport-airspace capacity, and recharge: a mathematical model and efficient heuristic

Urban Air Mobility (UAM) is expected to become a new form of urban transportation, using electric vertical take-off and landing (eVTOL) vehicles to help reduce congestion and lower emissions. However, the operational complexity of UAM demands sophisticated planning that accounts for constraints unique to aerial environments. This study develops an integrated routing optimization framework for multi-eVTOL operations, considering battery limitations, vertiport and air corridor capacities, minimum turnaround times, and passenger pooling through stopovers. A mixed-integer linear programming (MILP) model is formulated to obtain optimal solutions for small-scale problems. To overcome scalability challenges, a heuristic algorithm named Dual-Structure Adaptive Genetic Algorithm (DSAGA) is proposed. DSAGA employs a dual-structure chromosome separating vertiport sequencing and turnaround time, alongside dynamic parameter adaptation across generations to enhance exploration and convergence. A case study is conducted to demonstrate the model’s applicability, followed by extensive numerical experiments. The results reveal that DSAGA consistently achieves near-optimal solutions with significantly reduced computational time compared to CPLEX and Particle Swarm Optimization (PSO) methods. Sensitivity analyses on stopover fare rates and battery capacities offer operational insights, highlighting trade-offs between service coverage and profitability. Furthermore, it explores the real-time adjustment of pre-scheduled UAM operations to accommodate on-demand passenger requests. Overall, the proposed framework and solution approach contribute practical methodologies for realizing scalable, resilient, and customer-oriented UAM systems, bridging a critical gap between conceptual planning and real-world operational needs.