Identifying challenges in maintenance planning for on-demand UAM fleets using agent-based simulations

The novel aircraft architectures for Urban Air Mobility (UAM), combined with pure on-demand operations, mean a significant change in aircraft operation and maintenance compared to traditional airliners. Future flight missions and related variables such as the aircraft position or utilisation are unknown for on-demand operation. Consequently, existing methods to optimise aircraft assignment and maintenance planning cannot be transferred. This study examines the behaviour of an aircraft fleet in an on-demand UAM transport system regarding the interlinking between operation and maintenance. Initially, a potential maintenance schedule for UAM vehicles is deduced. A transport and maintenance simulation is introduced where aircraft are modelled as agents servicing a simple network. As aircraft reach their maintenance intervals, they transfer to one of the maintenance bases and compete for that resource. Since that competition can result in avoidable waiting times, the maintenance costs are extended by running costs for the bases and opportunity costs for missed revenue during these waiting periods. Opportunity costs are cost drivers. To reduce the waiting times, two operational approaches are examined: Extending the opening hours of the maintenance facilities and checking the aircraft earlier to reduce simultaneous maintenance demand. While an extension of operating hours reduces the overall maintenance costs, the adjustment of tasks is more effective to lower waiting times. Thus, an improved system needs to use a combined approach. That combination results in overall maintenance costs of approximately $ 58 per flight hour of which about seven percent account for the opportunity costs

Towards Minimum Expenditure MRO Concepts for UAM trough Vehicle Design and Operational Modelling

Well-established aviation companies, start-ups, and governmental institutions aim to lift Urban Air Mobility (UAM) into the skies. The novel aircraft architecture combined with on-demand operations servicing a small network means a significant change in its operation and maintenance requirements compared to commercial airlines. On-demand flights exhibit non-deterministic behaviour meaning that future flight missions and related variables such as the aircraft position and Flight Hours (FHs) are unknown. Consequently, existing methods to optimise aircraft assignment and maintenance planning cannot be transferred directly from traditional airlines. This study attempts to picture and understand the behaviour of an aircraft fleet in an on-demand UAM transport system regarding the interlinking between operation and maintenance. Hereby, the most significant parameters will be identified and the optimum within the system’s limitations will be determined. In order to address this, a transport and maintenance simulation is introduced, in which aircraft are modelled as agents and service flight missions within a simple network. As soon as hard time maintenance intervals, limited by FHs or flight cycles, are fully utilized, aircraft transfer to a maintenance base. As maintenance bases have a limited capacity for simultaneous checks, vehicles compete with each other for that limited resource, which can cause waiting times. To understand the impact on the whole technical operational ecosystem, the maintenance costs are extended to include running costs of the maintenance bases and opportunity costs for times aircraft cannot generate revenue, such as waiting time for a check. The most important output parameters to evaluate the quality of the simulation are the maintenance costs and the network transport capacity. Opportunity costs are identified as cost driver and primarily depend on the waiting time for maintenance checks. With changes in the operation of aircraft, the waiting time can be influenced. Two simple approaches to decrease the fleet waiting time and thereby reduce the maintenance costs are presented. One option is to trade a certain percentage of the vehicle’s remaining useful lifetime for earlier checks to avoid waiting times. With the other option, the way how aircraft are assigned to missions is altered. Both approaches can reduce bottlenecks at the maintenance bases. The system’s optimum combines both approaches and results in a good interlocking, with maintenance costs in the range of approx. $ 60/FH. About two-thirds of the maintenance costs account for the actual labour and material costs during the checks, the remaining third is the sum of the opportunity costs and the running expenses