- septiembre 14, 2007 - 16:00
- ZLC, Lecture Room A1
Department of Economics and Business
Universitat Pompeu Fabra
“On Upper Bounds for Network Revenue Management”
Empirically, tighter upper bounds to the dynamic programming value function seem to lead to better bid-prices – i.e., better revenues. So obtaining tighter upper bounds is a worthwhile research goal. The Randomized Linear Programming method for Network RM is a very simple and effective method that is based on the Perfect Hindsight (PHS) upper bound obtained from simulating the forecasts. In this work we relate the PHS upper bound to recent work by Adelman and the Lagrangian bound of Topaloglu. We also extend our work to choice network RM.
Kalyan Talluri is a ICREA Research Professor at the Universitat Pompeu Fabra in Barcelona, in the Economics and Business department. He got his Masters from Purdue University and a Ph.D in Operations Research from MIT. He as held visiting positions at Kellogg School, Northwestern University and INSEAD. He is the co-author of the book “Theory and Practice of Revenue Management”.