An effective dynamic decision policy for the revenue management of an airline flight

Abstract

Airline companies normally classify the seats of a cabin class into a number of fare classes. Customers of different fare classes arrive randomly during the booking horizon. Every time a customer in a certain fare class arrives, the airline company must decide promptly whether to fulfill or reject the request. To increase revenues, the airline company may reject certain lower-fare class customer requests and reserve the seats for future higher-fare class customers. However, rejecting too many lower-fare class customers may result in empty seats when the flight takes off. Given the multiple fare classes of a flight and the non-homogeneous Poisson customer arrival process in each fare class, and with the aim of maximizing the revenue of the flight, this study develops and tests two heuristic approaches – the dynamic seat rationing (DSR) decision policy and the expected revenue gap (ERG) decision policy – to help the airline make a fulfillment-or-rejection decision when a customer arrives. The simulation experiments show that ERG performs best among all tested approaches and, on average, the revenue from the ERG being merely 0.8% less than that of the optimal decision made with perfect information. Moreover, the ERG is very robust under various problem conditions.