Re-solving stochastic programming models for airline revenue management

Lijian Chen, Tito Homem-de-Mello

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)91-114
Number of pages24
JournalAnnals of Operations Research
Volume177
Issue number1
DOIs
StatePublished - 2010
Externally publishedYes

Keywords

  • Multi-stage models
  • Revenue management
  • Stochastic programming

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