Choice Based Revenue Management for Parallel Flights

This paper describes a revenue management problem of a major airline that operates in a very competitive market involving two major hubs and having more than 30 parallel daily flights. We consider choice based stochastic assortment optimization problems to maximize the expected revenue for the airline. The inputs include models of booking arrival rates, competitor assortment selection probabilities, customers' booking choices among the airline's own flights as well as competitors' flights, booking-to-ticketing conversion probabilities, and go-show and no-show probabilities. We build a variety of booking choice models to incorporate unobserved heterogeneous customer preferences for different departure times. The way departure time preferences are modeled dramatically affects price sensitivity estimates, and therefore the modeling of heterogeneous departure time preferences matters. We also show that customer choice behavior exhibits discontinuities, with much greater demand for the cheapest alternative than for the second cheapest alternative even when the price difference is small, and much greater demand for fully refundable tickets than almost fully refundable tickets. We formulate a deterministic (fluid) optimization problem corresponding to each of the booking choice models, and we show that in some cases these problems can be solved efficiently even when the discontinuities cause violation of the independence from irrelevant alternatives property. The resulting solutions are used to determine assortment selection policies for the stochastic problem. Simulation studies show that several of these policies generate significantly more revenue than the airline's existing policy, and that the improved performance of these policies is robust with respect to misspecification errors as well as with respect to errors in parameter estimates.