Five-stage procedure for the evaluation of simulation models through statistical techniques
WSC '96 Proceedings of the 28th conference on Winter simulation
Bayesian model selection when the number of components is unknown
Proceedings of the 30th conference on Winter simulation
Bootstrapping and validation of metamodels in simulation
Proceedings of the 30th conference on Winter simulation
Interactive implementation of optimal simulation experiment designs
Proceedings of the 30th conference on Winter simulation
Regression metamodeling in simulation using Bayesian methods
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Variance-based sampling for cycle time: throughput confidence intervals
WSC '04 Proceedings of the 36th conference on Winter simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Estimation of percentiles of cycle time in manufacturing simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Estimating expected completion times with probabilistic job routing
Proceedings of the 38th conference on Winter simulation
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Simulation metamodels for modeling output distribution parameters
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Stochastic kriging for simulation metamodeling
Proceedings of the 40th Conference on Winter Simulation
Sequential designs for simulation experiments: nonlinear regression metamodeling
MIC '07 Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control
Stochastic Kriging for Simulation Metamodeling
Operations Research
Better simulation metamodeling: the why, what, and how of stochastic kriging
Winter Simulation Conference
Kriging metamodel with modified nugget-effect: The heteroscedastic variance case
Computers and Industrial Engineering
Optimal allocation of runs in a simulation metamodel with several independent variables
Operations Research Letters
Bayesian Kriging Analysis and Design for Stochastic Simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Reinsch's smoothing spline simulation metamodels
Proceedings of the Winter Simulation Conference
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Simulation experiments for analysing the steady-state behaviour of queueing systems over a range of traffic intensities are considered, and a procedure is presented for improving their design. In such simulations the mean and variance of the response output can increase dramatically with traffic intensity; the design has to be able to cope with this complication. A regression metamodel of the likely mean response is used consisting of two factors, namely, a low-degree polynomial and a factor accounting for the exploding mean as the traffic intensity approaches its saturation. The best choice of traffic intensities at which to make simulation runs depends on the variability of the simulation output, and this variability is estimated using analytical heavy traffic results. The optimal numbers of customers simulated at each traffic intensity are built up using a multistage procedure. The asymptotic properties of the procedure are investigated theoretically. The procedure is shown to be robust and to be more efficient than more naive procedures. A result of note is that even when the range of interest includes high traffic intensities, the highest traffic load simulated should remain well away from its upper limit; but the number of customers simulated should be concentrated at the higher traffic intensities used. Empirical results are included for simulations of a single server queue with different priority rules and for a complicated queueing network. These results support the theoretical results, demonstrating that the proposed procedure can increase the accuracy of the estimated metamodel significantly compared with more naive methods.