A methodology for optimization of simulation models is presented. The methodology is
based on a genetic algorithm in conjunction with an indifference-zone ranking and selection …

We present a review of methods for optimizing stochastic systems using simulation. The
focus is on gradient based techniques for optimization with respect to continuous decision …

Simulation optimization can be defined as the process of finding the best input variable
values from among all possibilities without explicitly evaluating each possibility. The …

We present a new random search method for solving simulation optimization problems. Our
approach emphasizes the need for maintaining the right balance between exploration and …

We give a tutorial introduction to simulation optimization. We begin by classifying the
problem setting according to the decision variables and constraints, putting the setting in the …

We propose fully sequential indifference-zone selection procedures that are specifically for
use within an optimization-via-simulation algorithm when simulation is costly and partial or …

Simulation optimization is rapidly becoming a mainstream tool for simulation practitioners.
Simulation optimization is the practice of linking an optimization method with a simulation …

We provide a descriptive review of the main approaches for carrying out simulation
optimization, and sample some recent algorithmic and theoretical developments in …

A new algorithm class is presented for optimization of stochastic simulation models. The
algorithms, which combine generalized pattern search (GPS) with ranking and selection …

Consider the problem of using simulation to optimize the performance of a stochastic system
with respect to a number of decision variables. In the past, a considerable effort haa been …