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Revenue management can enable airline corporations to maximize the revenuegenerated from each scheduled flight departing in their transportation networkby means of finding the optimal policies for differential pricing, seatinventory control and overbooking. As different demand segments in the markethave different Willingness-To-Pay (WTP), airlines use differential pricing,booking restrictions, and service amenities to determine different fare classesor products targeted at each of these demand segments. Because seats arelimited for each flight, airlines also need to allocate seats for each of thesefare classes to prevent lower fare class passengers from displacing higher fareclass ones and set overbooking limits in anticipation of cancellations andno-shows such that revenue is maximized. Previous work addresses these problemsusing optimization techniques or classical Reinforcement Learning methods. Thispaper focuses on the latter problem - the seat inventory control problem -casting it as a Markov Decision Process to be able to find the optimal policy.Multiple fare classes, concurrent continuous arrival of passengers of differentfare classes, overbooking and random cancellations that are independent ofclass have been considered in the model. We have addressed this problem usingDeep Q-Learning with the goal of maximizing the reward for each flightdeparture. The implementation of this technique allows us to employ largecontinuous state space but also presents the potential opportunity to test onreal time airline data. To generate data and train the agent, a basicair-travel market simulator was developed. The performance of the agent indifferent simulated market scenarios was compared against theoretically optimalsolutions and was found to be nearly close to the expected optimal revenue.

Autonomous Airline Revenue Management: A Deep Reinforcement Learning Approach to Seat Inventory Control and Overbooking