TITLE:  Rare Event Simulation for Many Server Queues

SPEAKER: Dr. Jose Blanchet

ABSTRACT:

Our focus is on the development of provably efficient simulation
algorithms for estimating large deviations probabilities (such as
overflow probabilities) in the context of many server queues. These
types of systems, which have been the subject of much investigation in
recent years, pose interesting challenges from a rare event simulation
standpoint, given their measure valued state descriptor. We shall
explain a technique that has the following elements. First, it
introduces a pivotal set that is suitable chosen to deal with
boundary-type behavior, which is common in the analysis of queueing
systems. Second, it takes advantage of Central Limit Theorem
approximations that have been developed recently for these types of
systems and third it use a novel bridge-sampling approach in order to
describe an asymptotically optimal (in certain sense) importance
sampling scheme. This work provides the first systematic approach to
develop provably efficient rare-event simulation methodology for these
types of systems.

(Joint work with P. Glynn and H. Lam)

Bio:

Jose Blanchet is a faculty member of the IEOR at Columbia University.
Jose holds a Ph.D. in Management Science and Engineering from Stanford
University. Prior to joining Columbia he was a faculty member in the
Statistics Department at Harvard University. Jose is a recipient of the
2009 Best Publication Award given by the INFORMS Applied Probability
Society and a CAREER award in Operations Research given by NSF in 2008.
He worked as an analyst in Protego Financial Advisors, a leading
investment bank in Mexico. He has research interests in applied
probability and Monte Carlo methods. He serves in the editorial board of
Advances in Applied Probability, Journal of Applied Probability, QUESTA
and TOMACS.