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Solving for an optimal airline yield management policy via statistical learning
Author(s) -
Chen Victoria C. P.,
Günther Dirk,
Johnson Ellis L.
Publication year - 2003
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00386
Subject(s) - heuristics , yield management , operations research , revenue , profit (economics) , revenue management , dynamic programming , yield (engineering) , computer science , mathematical optimization , multivariate statistics , industrial organization , economics , operations management , microeconomics , engineering , mathematics , machine learning , finance , materials science , metallurgy
Summary. 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.