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Discrete-Event System Simulation
This book provides a basic treatment of one of the most widely used operations research tools: discrete-event simulation. Prerequisites are calculus, probability theory, and elementary statistics.Expand
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Stochastic kriging for simulation metamodeling
TLDR
We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Expand
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Stochastic kriging for simulation metamodeling
TLDR
We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Expand
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Discrete Optimization via Simulation Using COMPASS
TLDR
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. Expand
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Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large
TLDR
We address the problem of finding the simulated system with the best (maximum or minimum) expected performance when the number of alternatives is finite, but large enough that ranking-and-selection (R&S) procedures may require too much computation. Expand
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A fully sequential procedure for indifference-zone selection in simulation
TLDR
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. Expand
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Using Ranking and Selection to "Clean Up" after Simulation Optimization
TLDR
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. Expand
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Autoregressive to anything: Time-series input processes for simulation
TLDR
We develop a model for representing stationary time series with arbitrary marginal distributions and autocorrelation structures and describe how to generate data based upon our model for use in a simulation. Expand
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Using common random numbers for indifference-zone selection and multiple comparisons in simulation
We present a general recipe for constructing experiment design and analysis procedures that simultaneously provide indifference-zone selection and multiple-comparison inference for choosing the bestExpand
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Numerical Methods for Fitting and Simulating Autoregressive-to-Anything Processes
TLDR
An ARTA (AutoRegressive-to-Anything)Process is a time series with arbitrary marginal distribution and autocorrelation structure specified through finite lag p. Expand
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