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TITLE: Metamodel-Assisted Input Model Uncertainty Characterization
SPEAKER: Dr. Russell Barton
ABSTRACT:
Discrete-event simulation input model uncertainty affects the process of constructing confidence intervals for the mean response of a system represented by a stochastic simulation. Uncertainty is introduced when input models have been estimated from “real-world” data. The confidence interval should account for both uncertainty about the input models and stochastic noise in the simulation output, but standard practice only accounts for the stochastic noise. Bootstrapping has been used to characterize input model uncertainty, but bootstrap approaches that use simulation replications can be computationally expensive and may fail a requirement for the asymptotic validity of the bootstrap. This talk presents a metamodel-assisted bootstrapping strategy, and compares its performance relative to other approaches for dealing with input uncertainty. This talk presents joint work with Barry Nelson and Wei Xie.
Bio:
Russell Barton is a professor in the Department of Supply Chain and Information Systems at the Pennsylvania State University. He recently completed a two-year assignment as Program Director for Manufacturing Enterprise Systems and Service Enterprise Systems at the U.S. National Science Foundation. Before entering academia, he spent twelve years in industry. He is a past president of the INFORMS Simulation Society and serves on the advisory board for the INFORMS Quality Statistics and Reliability section. He is a senior member of IIE and IEEE. His research interests include applications of statistical and simulation methods to system design and to product design, manufacturing and delivery.
Pizza and drinks will be provided.
