Robust Controls for Network Revenue Management
43 Pages Posted: 5 Oct 2007 Last revised: 19 Jul 2012
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: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
An Integrated Approach to Single-Leg Airline Revenue Management: The Role of Robust Optimization
By S. Ilker Birbil, J. B. G. Frenk, ...
-
Monopoly Pricing with Limited Demand Information
By Serkan Eren and Costis Maglaras
-
Monopoly Pricing with Limited Demand Information
By Serkan Eren and Costis Maglaras
-
By Andrew Lim, J. George Shanthikumar, ...
-
Dynamic Pricing Through Sampling Based Optimization
By Ruben Lobel and Georgia Perakis
-
Dynamic Pricing with Financial Milestones: Feedback-Form Policies
By Omar Besbes and Costis Maglaras
-
Overbooking and Fare-Class Allocation with Limited Information
By Yingjie Lan, Michael O. Ball, ...
-
A Maximum Entropy Approach to the Newsvendor Problem with Partial Information
By Jonas Andersson, Kurt Jornsten, ...