Robust Revenue Management

Start: 05/07/2009 - 4:15pm
End  : 05/07/2009 - 5:15pm

Statistics/OR/Math Finance Seminar

Guillaume Roels (UCLA Anderson School of Management)


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 talk, I 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
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. Our models are scalable to solve practical
problems because they combine efficient (exact or heuristic) solution
methods with very modest data requirements.
[Joint work with Georgia Perakis]

Beckman Auditorium (Bk B126), Harvey Mudd College