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ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 13 Issue 2, April 2003

Guest editorial
Michael Fu, Barry Nelson
Pages: 105-107
DOI: 10.1145/858481.858482

Variable-sample methods for stochastic optimization
Tito Homem-De-Mello
Pages: 108-133
DOI: 10.1145/858481.858483
In this article we discuss the application of a certain class of Monte Carlo methods to stochastic optimization problems. Particularly, we study variable-sample techniques, in which the objective function is replaced, at each iteration,...

Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures: A survey
James R. Swisher, Sheldon H. Jacobson, Enver Yücesan
Pages: 134-154
DOI: 10.1145/858481.858484
An important use for discrete-event simulation models lies in comparing and contrasting competing design alternatives without incurring any physical costs. This article presents a survey of the literature for two widely used classes of statistical...

A combined procedure for optimization via simulation
Juta Pichitlamken, Barry L. Nelson
Pages: 155-179
DOI: 10.1145/858481.858485
We propose an optimization-via-simulation algorithm for use when the performance measure is estimated via a stochastic, discrete-event simulation, and the decision variables may be subject to deterministic linear integer constraints. Our...

Two-timescale simultaneous perturbation stochastic approximation using deterministic perturbation sequences
Shalabh Bhatnagar, Michael C. Fu, Steven I. Marcus, I-Jeng Wang
Pages: 180-209
DOI: 10.1145/858481.858486
Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effective for high-dimensional simulation optimization problems. The main idea is to estimate the gradient using simulation output performance measures at...