Skip to main content
Re-Solving Stochastic Programming Models for Airline Revenue Management
Annals of Operations Research
  • Lijian Chen, University of Dayton
  • Tito Homem-de-Mello, Northwestern University
Document Type
Publication Date
We 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.
Inclusive pages
Document Version

The document available for download is the authors' accepted manuscript. Some differences may appear between this version and the published article. As such, researchers wishing to quote directly from this resource are advised to consult the version of record.

Permission documentation is on file.

This research was supported by the National Science Foundation under grant DMI-0115385.

Springer Science and Business Media
Place of Publication
Peer Reviewed
Citation Information
Lijian 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: