Mobility on Demand (MoD) is a new paradigm of personal mobility that responds to passengers' demands in real time, and Urban Air Mobility (UAM) is an area of MoD enabled by advances in electric vertical take-off and landing aircraft. This demand-responsive nature of MoD is a challenge in optimally scheduling vehicles, which has attracted much attention in recent years. However, there is a lack of research in the MoD scheduling literature: a homogeneous fleet is assumed, but it is not necessarily true all the time. Hence, this study proposes a novel formulation of the scheduling problem for UAM with a heterogeneous fleet, and presents Particle Swarm Optimization and Genetic Algorithm that utilize a Greedy Algorithm to keep solutions feasible. The proposed algorithms are implemented with a Model Predictive Control scheme to effectively manage the demand-responsive nature. As a result, the proposed algorithms can find a near-optimal solution in a short time. Using the algorithms, a numerical experiment with six different fleet mixes is conducted, and impacts of fleet heterogeneity are analyzed. As a result, it is shown that the fleet heterogeneity affects both the quality of service and operational efficiency, and there is a trade-off: the more vehicles and seats, the better the service, but the less efficient it is.

A passenger’s experience is becoming a key metric to evaluate the air transportation system’s performance. Efficient and robust tools to handle airport operations are needed along with a better understanding of passengers’ interests and concerns. This paper is concerned with airport gate scheduling for improved passenger experience while ensuring robust airside operations. Three metrics accounting for passengers, aircraft, and operations are presented. Tradeoffs between these metrics are analyzed, and a balancing objective function is proposed. Numerical simulations show that the balanced objective can improve the efficiency of passenger movement within the landside terminals and aircraft traffic flows on ramps, as well as the robustness of gate operations.