A Statistical Modeling Approach to Airline Revenue Management

Revenue management (RM) aims to maximize a company’s revenue by allocating the right seat to the right customer. In this paper, we present an approach based on a Markov decision process (MDP) formulation. Our approach involves an off-line phase that derives a policy for accepting/rejecting customer booking requests, and an on-line phase that conducts the actual decisions as the booking requests arrive. To enable a computationally-tractable solution method, the off-line phase consists of three components: (1) identification of realistic ranges of remaining seat capacity at different points in time, (2) solutions to deterministic and stochastic linear programming problems that provide upper and lower bounds, respectively, on the MDP value function, and (3) estimation of the upper and lower bound value functions using statistical modeling. This value function approximation is then used to determine the RM accept/reject policy. Prior versions of this statistical modeling approach have employed remaining seat capacity ranges from zero to the capacity of the aircraft. In reality, actual remaining capacities are near capacity when the booking process begins and near zero when the flights depart. Thus, our modified version uses realistic ranges to enable a more accurate statistical model, leading to a better RM policy.