Solving for an optimal airline yield management policy via statistical learning

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. Copyright 2003 Royal Statistical Society.