Re-Solving Stochastic Programming Models for Airline Revenue ManagementAnnals of Operations Research
AbstractWe study some mathematical programming formulations for the origin-destination model in airline revenue management. In particular, we focus on the traditional probabilistic model proposed in the literature. The approach we study consists of solving a sequence of two-stage stochastic programs with simple recourse, which can be viewed as an approximation to a multi-stage stochastic programming formulation to the seat allocation problem. Our theoretical results show that the proposed approximation is robust, in the sense that solving more successive two-stage programs can never worsen the expected revenue obtained with the corresponding allocation policy. Although intuitive, such a property is known not to hold for the traditional deterministic linear programming model found in the literature. We also show that this property does not hold for some bid-price policies. In addition, we propose a heuristic method to choose the re-solving points, rather than re-solving at equally spaced times as customary. Numerical results are presented to illustrate the effectiveness of the proposed approach.
CopyrightCopyright © 2010, Springer Science and Business Media.
PublisherSpringer Science and Business Media
Place of PublicationGermany
Sponsoring AgencyNational Science Foundation
Citation InformationLijian Chen and Tito Homem-de-Mello. "Re-Solving Stochastic Programming Models for Airline Revenue Management" Annals of Operations Research Vol. 177 Iss. 1 (2010)
Available at: http://works.bepress.com/lijian_chen/7/