__Claremont Graduate University__ | __Claremont McKenna__ | __Harvey Mudd__ | __Pitzer__ | __Pomona__ | __Scripps__

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Copyright © 2011 Claremont Center for the Mathematical Sciences

When

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

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

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

Category

Statistics/OR/Math Finance Seminar

Speaker

Guillaume Roels (UCLA Anderson School of Management)

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 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]

Where

Beckman Auditorium (Bk B126), Harvey Mudd College