Heuristic Approach for Aircraft Assignment and Maintenance Scheduling of On-demand Urban Air Mobility Vehicles

View Video Presentation: https://doi.org/10.2514/6.2023-3624.vid The development vehicles and concepts for Urban Air Mobility (UAM) has been encouraged the technology enhancements such as the introduction of the 5G standard for mobile networks or continuous improvements in battery performance. While past research efforts have covered social acceptance of airborne vehicles in urban areas, autonomous piloting, their integration into airspace and the development of the aircraft themselves, maintenance and scheduling aspects have hardly been considered thus far. Simultaneously, the nature of on-demand operation for Urban Air Mobility Vehicles (UAMVs) differs from traditional airline operations as future utilization and aircraft positions are only known a few minutes in advance. As a consequence, established concepts, known as Aircraft Maintenance Routing Problems (AMRPs) that harmonize the routing of airliners with maintenance activities, cannot be applied to the maintenance scheduling of UAMVs. Within this study, a heuristic approach is presented to assign aircraft to flight missions under consideration of their imminent maintenance needs. The key principle of this assignment strategy is the use of a weighted bidding model that incorporates aspects of dead head flight, as well as avoidable waiting times for the completion of maintenance tasks. Ultimately, this approach will reduce conflicting situations for limited maintenance capacities for flight-hour-driven scheduled maintenance checks. The heuristic is implemented into a discrete events flight simulation, simulating 160 UAMVs in a generic UAM network with two off-grid maintenance facilities with a simulation time span of one year. The result of the proposed heuristic is compared to a baseline scenario in terms of accepted flight request rate, the average waiting time for maintenance completion, the average waiting time before commencement of a mission and the maintenance base utilization. Furthermore, different simulations are run with varying weighting factors for maintenance and operating aspects to examine possible improvements towards the heuristic’s performance. Hereby, the interlinking between operation and maintenance could be improved with the usage of the maintenance scheduling heuristic. As a result, the fleet maintenance waiting time could be reduced by a factor of 2.6 and the share of missed flight requests due to maintenance downtimes could be limited to one percent. Additionally, the utilization of the maintenance bases is also increased through the adapted scheduling and assignment heuristic.