Solving for an optimal airline yield management policy via statistical learning

Solving for an optimal airline yield management policy via statistical learning

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Article ID: iaor20042191
Country: United Kingdom
Volume: 52
Issue: 1
Start Page Number: 19
End Page Number: 30
Publication Date: Apr 2003
Journal: App Stats
Authors: , ,
Keywords: markov processes, programming: dynamic, yield management
Abstract:

The yield management (YM) problem considers the task of maximizing a company's revenue. For the competitive airline industry, profit margins depend on a good YM policy. Research on airline YM is abundant but still limited to heuristics and small cases. We address the YM problem for a major domestic airline carrier's hub-and-spoke network, involving 20 cities and 31 flight legs. This is a problem of realistic size since airline networks are usually separated by hub cities. Our method is a variant of the orthogonal array experimental designs and multivariate adaptive regression splines stochastic dynamic programming method. Our method is demonstrated to outperform state of the art YM methods.

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