Performance of multiple cabin optimization methods in airline revenue management

Abstract

Although many airlines offer seats in multiple cabins (economy vs. premium classes) with different service quality, previous work on airline revenue management has focused on treating the cabins separately. In this thesis, we develop several single-leg multiple cabin revenue management optimization algorithms. We extend two different single-leg separate cabin dynamic programming algorithms to the multiple cabin case, and also present three Expected Marginal Seat Revenue (EMSR) based heuristics and a dynamic programming decomposition heuristic. We then evaluate the revenue and passenger mix performance of the different algorithms using the Passenger Origin-Destination Simulator (PODS) which simulates competitive markets with passenger choice of fare options and cabin. We first test the methods in a simple single market network and then in a more realistic complex network. We find that multiple cabin methods do not lead to a systematic revenue increase. Indeed, simulation results show that the performance of the different methods ranges from a decrease of 9.6% to an increase of 2.4% in revenues. The discrepancies in performance between the different methods are explained by the trade-off between revenue gains from additional economy bookings and the losses from displaced premium passengers. Further, we observe that successful methods lead to a revenue increase by accepting additional bookings in top economy classes rather than in low economy classes. Finally, the poor performance of the dynamic programming methods tested is due to a misalignment between the underlying assumptions of the algorithms and the reality of the booking and passenger choice process.