Re-solving stochastic programming models for airline revenue management

Lijian Chen () and Tito Homem- de-Mello ()

Annals of Operations Research, 2010, vol. 177, issue 1, 114 pages

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

Keywords: Stochastic programming; Multi-stage models; Revenue management (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (18)

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DOI: 10.1007/s10479-009-0603-7

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