Paper Info
Title
Simulation optimization: A tutorial overview and recent developments in gradient-based methods
Abstract
We provide a tutorial overview of simulation optimization methods, including statistical ranking & selection (R&S) methods such as indifference-zone procedures, optimal computing budget allocation (OCBA), and Bayesian value of information (VIP) approaches; random search methods; sample average approximation (SAA); response surface methodology (RSM); and stochastic approximation (SA). In this paper, we provide high-level descriptions of each of the approaches, as well as some comparisons of their characteristics and relative strengths; simple examples will be used to illustrate the different approaches in the talk. We then describe some recent research in two areas of simulation optimization: stochastic approximation and the use of direct stochastic gradients for simulation metamodels. We conclude with a brief discussion of available simulation optimization software.
Year
DOI
Venue
2014
10.1109/WSC.2014.7019875
Winter Simulation Conference
Keywords
Field
DocType
Bayes methods,approximation theory,gradient methods,random processes,simulation,statistical analysis,stochastic processes,Bayesian value of information,OCBA,R&S method,RSM,SAA,VIP approach,gradient-based methods,high-level description,indifference-zone procedure,optimal computing budget allocation,random search method,response surface methodology,sample average approximation,simulation metamodel,simulation optimization software,statistical ranking & selection,stochastic approximation,stochastic gradient,tutorial overview
Sample average approximation,Random search,Mathematical optimization,Stochastic optimization,Ranking,Computer science,Software,Stochastic approximation,Response surface methodology
Conference
ISSN
ISBN
Citations 
0891-7736
978-1-4673-9741-4
9
PageRank 
References 
Authors
0.83
23
4
Name
Order
Citations
PageRank
Marie Chau1101.52
Michael C. Fu21161128.16
Huashuai Qu3444.55
Ilya O. Ryzhov4162.41