简介： 
In stochastic simulation settings, techniques such as perturbation analysis and the likelihood ratio method allow one to obtain unbiased estimates of the gradient of output responses as a function of input parameters without having to resort to finite differences and resimulation. Recently, several new approaches have been proposed to incorporate these direction gradients into existing simulation optimization approaches such as response surface methodology, stochastic kriging, and stochastic approximation. We provide an overview of some of these approaches.

Bio
MICHAEL C. FU is Ralph J. Tyser Professor of Management Science in the Decision, Operations and Information Technologies department of the Robert H. Smith School of Business, with a joint appointment in the Institute for Systems Research and an affiliate appointment in the Department of Electrical & Computer Engineering (both in the Clark School of Engineering), all at the University of Maryland.  He received an S.B. in mathematics and S.B./S.M. in EECS from MIT and an M.S. and Ph.D. in applied mathematics from Harvard University.  His research interests include simulation optimization and applied probability, particularly with applications towards supply chain management and financial engineering. In 2004 he was named a Distinguished Scholar-Teacher at the University of Maryland. He is a Fellow of INFORMS and IEEE.