  This work considers the scheduling problem for Urban Air Mobility (UAM)
vehicles travelling between origin-destination pairs with both hard and soft
trip deadlines. Each route is described by a discrete probability distribution
over trip completion times (or delay) and over inter-arrival times of requests
(or demand) for the route along with a fixed hard or soft deadline. Soft
deadlines carry a cost that is incurred when the deadline is missed. An online,
safe scheduler is developed that ensures that hard deadlines are never missed,
and that average cost of missing soft deadlines is minimized. The system is
modelled as a Markov Decision Process (MDP) and safe model-based learning is
used to find the probabilistic distributions over route delays and demand.
Monte Carlo Tree Search (MCTS) Earliest Deadline First (EDF) is used to safely
explore the learned models in an online fashion and develop a near-optimal
non-preemptive scheduling policy. These results are compared with Value
Iteration (VI) and MCTS (Random) scheduling solutions.
