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We describe a model for representing random vectors whose component random variables have arbitrary marginal distributions and correlation matrix, and describe how to generate data based upon this model for use in a stochastic simulation. The central idea is to transform a multivariate normal random vector into the desired random vector, so we refer to(More)
We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide flexible, interpolation-based metamodels of simulation output performance measures as functions of the controllable design or decision variables. To accomplish this we characterize(More)
In this paper we address the problem of nding the simulated system with the best (maximum or minimum) expected performance when the number of alternatives is nite, but large enough that ranking-and-selection procedures may require too much computation to be practical. Our approach is to use the data provided by the rst stage of sampling in a(More)
We present procedures for selecting the best or near-best of a finite number of simulated systems when best is defined by maximum or minimum expected performance. The procedures are appropriate when it is possible to repeatedly obtain small, incremental samples from each simulated system. The goal of such a sequential procedure is to eliminate, at an early(More)
We propose an optimization-via-simulation algorithm, called COMPASS, for use when the performance measure is estimated via a stochastic, discrete-event simulation, and the decision variables are integer ordered. We prove that COMPASS converges to the set of local optimal solutions with probability 1 for both terminating and steady-state simulation, and for(More)
An ARTA (AutoRegressive to Anything) Process is a time series with arbitrary marginal distribution and autocorrelation structure speciied through nite lag p. We develop an eecient numerical method for tting ARTA processes and discuss its implementation in the software ARTAFACTS. We also present the software ARTAGEN that generates observations from ARTA(More)
In this paper we address the problem of finding the simulated system with the best (maximum or minimum) expected performance when the number of systems is large and initial samples from each system have already been taken. This problem may be encountered when a heuristic search procedure—perhaps one originally designed for use in a deterministic(More)
We describe the basic principles of ranking and selection, a collection of experiment-design techniques for comparing " populations " with the goal of finding the best among them. We then describe the challenges and opportunities encountered in adapting ranking-and-selection techniques to stochastic simulation problems, along with key theorems, results and(More)