Statistical Modeling Approach To Airline Revenue Management With Overbooking

A Revenue Management (RM) method in the airline industry plays a very important role in maximizing revenue under various uncertainty issues, like customer demand, the number of seats to be maintained in inventory, the number of seats to be overbooked, etc. In this dissertation, a Markov decision process (MDP) based approach using statistical modeling is presented. 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. We also consider overbooking, no-shows and cancellations and estimate the optimal number of seats to be overbooked using a hybrid approach that combines Newton's and steepest ascent method. The extended statistical modeling approach in this dissertation consists of three modules: (1) the revised statistical modeling module, (2) the overbooking module, and (3) the availability processor module. The first two modules are conducted off-line to identify optimal overbooking pads and derive a policy for accepting/rejecting customer booking requests. The last module is occurs on-line to conduct the actual decisions as the booking requests arrive. To enable a computationally-tractable solution method, the revised statistical modeling module, under an assumed maximum overbooking pad of 20%, consists of three components: (1) simulation of the deterministic bid price approach to identify the 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. Next, the overbooking module identifies the optimal number of seats to be overbooked. Finally, the value function approximations are used with the optimal overbooking pads to determine the RM accept/reject policy.