Correspondence: Peter Belobaba, MIT International Center for Air Transportation, 77 Mass. Ave., Room 33–215, Cambridge, MA 02139 USA
E-mail: belobaba@mit.edu

¹is Chief Scientist in the Revenue Management Development department at Scandinavian Airlines System (SAS). He is responsible for developing methods and strategy for revenue management systems at SAS, including the overall design and methodologies of the O&D forecasting and optimization systems. His recent work has focused on methodologies for O&D optimization in semi-restricted fare structures and estimating price elasticities. Dr Fiig holds a PhD in Mathematics and theoretical Physics and a BA in Finance from the University of Copenhagen.

²is Head of Operations Research and Strategy in the Revenue Management, Pricing and Distribution department of Swiss International Airlines. He holds a PhD in Theoretical Physics from ETH Zurich. He developed the concepts for the integrated O&D pricing and inventory control strategy used by Swiss.

³is currently president of Hopperstad Consulting. Previously, as Project Director in the Boeing Commercial Airplane Group, he was a principal in the development of passenger preference, fleet planning, scheduling and revenue management models. He is the author of numerous papers and presentations, many of which deal with the application of the Passenger Origin/Destination Simulator (PODS), which he developed at Boeing and for which Hopperstad Consulting now holds a license.

⁴is Principal Research Scientist at the Massachusetts Institute of Technology (MIT), where he teaches graduate courses on The Airline Industry and Airline Management. He is Program Manager of MIT's Global Airline Industry Program and Director of the MIT PODS Revenue Management Research Consortium. Dr Belobaba holds a Master of Science and a PhD in Flight Transportation Systems from MIT. He has worked as a consultant on revenue management systems at over 40 airlines and other companies worldwide.

Received 7 April 2009; Revised 7 April 2009; Published online 19 June 2009.

Abstract

This paper develops a theory for optimizing revenue through seat inventory control that can be applied in a variety of airline fare structures, including those with less restricted and fully undifferentiated fare products that have become more common in the recent past. We describe an approach to transform the fares and the demand of a general discrete choice model to an equivalent independent demand model. The transformation and resulting fare adjustment approach is valid for both static and dynamic optimization and extends to network revenue management applications. This transformation allows the continued use of the optimization algorithms and seat inventory control mechanisms of traditional revenue management systems, developed more than two decades ago under the assumption of independent demands for fare classes.