Robust Controls for Network Revenue Management

43 Pages Posted: 5 Oct 2007 Last revised: 19 Jul 2012

Georgia Perakis

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Guillaume Roels

University of California, Los Angeles (UCLA) - Decisions, Operations, and Technology Management (DOTM) Area

Date Written: December 1, 2010

Abstract

Revenue management models traditionally assume that future demand is unknown but can be described by a stochastic process or a probability distribution. Demand is, however, often difficult to characterize, especially in new or nonstationary markets. In this paper, we develop robust formulations for the capacity allocation problem in revenue management using the maximin and the minimax regret criteria under general polyhedral uncertainty sets. Our approach encompasses the following open-loop controls: partitioned booking limits, nested booking limits, displacement-adjusted virtual nesting, and fixed bid prices. In specific problem instances, we show that a booking policy of the type of displacement-adjusted virtual nesting is robust, both from maximin and minimax regret perspectives. Our numerical analysis reveals that the minimax regret controls perform very well on average, despite their worst-case focus, and outperform the traditional controls when demand is correlated or censored. In particular, on real large-scale problem sets, the minimax regret approach outperforms by up to 2% the traditional heuristics. The maximin controls are more conservative but have the merit of being associated with a minimum revenue guarantee. Our models are scalable to solve practical problems because they combine efficient (exact or heuristic) solution methods with very modest data requirements.

Keywords: revenue management, robust control

JEL Classification: R3

Suggested Citation

Perakis, Georgia and Roels, Guillaume, Robust Controls for Network Revenue Management (December 1, 2010). Available at SSRN: https://ssrn.com/abstract=1018518 or http://dx.doi.org/10.2139/ssrn.1018518

Georgia Perakis

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

77 Massachusetts Ave.
E62-416
Cambridge, MA 02142
United States

Guillaume Roels (Contact Author)

University of California, Los Angeles (UCLA) - Decisions, Operations, and Technology Management (DOTM) Area ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

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