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Re-solving stochastic programming models for airline revenue management

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  • Lijian Chen
  • Tito Homem-de-Mello

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. Copyright Springer Science+Business Media, LLC 2010

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  • Lijian Chen & Tito Homem-de-Mello, 2010. "Re-solving stochastic programming models for airline revenue management," Annals of Operations Research, Springer, vol. 177(1), pages 91-114, June.
    • Handle: RePEc:spr:annopr:v:177:y:2010:i:1:p:91-114:10.1007/s10479-009-0603-7
    DOI: 10.1007/s10479-009-0603-7
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