So many factors, so little time…Simulation experiments in the frequency domain

Sanchez, Susan M.; Moeeni, Farhad; Sánchez, Paul

doi:10.1016/j.ijpe.2005.06.007

Cited by 25 publications

(17 citation statements)

References 23 publications

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“…According to [39], many studies in operational research use full factorial design due to its simplicity and because the technique allows the analyst to identify interactions between factors as well as their main effects.…”

Section: Full Factorial Designmentioning

confidence: 99%

Sensitivity Analysis in Discrete Event Simulation Using Design of Experiments

Montevechi¹,

Miranda²,

Friend³

2012

Discrete Event Simulations - Development and Applications

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Section: Full Factorial Designmentioning

confidence: 99%

Sensitivity Analysis in Discrete Event Simulation Using Design of Experiments

Montevechi¹,

Miranda²,

Friend³

2012

Discrete Event Simulations - Development and Applications

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“…To decide on the importance of the coefficients, one can use a graph of the SNR versus p that shows a spike for each important term at its term indicator frequencies (Sanchez et al, 2006). A formal analysis is also possible.…”

Section: Model Structure and Test Statisticmentioning

confidence: 99%

“…This is due to the fact that while the FDM method is able to screen the main effects, the interaction effects, the quadratic effects, etc., none of the other methods, except the factorial analysis, is able to do this. Sanchez et al (2006) also showed that FDM needs less data while it can screen factors as accurate as factorial experiments but it should be mention that FDM has been introduced only for discrete-event simulation models which is our focus in this article.…”

Section: Introductionmentioning

confidence: 99%

“…For example, they are all run oriented. This means that the required runs grow too fast as the number of input factors grows (Sanchez et al, 2006). In general, FDM in comparison to other available factor screening methods such as group screening, saturated, SB, SB-X, or CSB methods is relatively the most comprehensive method.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, Sanchez et al (2006) demonstrated that FDM requires even less data than the factorial designs when the output stream is correlated (Sanchez et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New Statistic to Increase Correctness in Simulation Factor Screening Using Frequency Domain Method

Sohi

Noorossana

Niaki

2012

Communications in Statistics - Theory and Methods

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Simulation models often include a large number of input factors, many of them may be unimportant to the output; justifying the use of factor screening experiments to eliminate unimportant input factors from consideration in later stages of analysis.With a large number of factors, the challenge is designing experiments so that total number of runs and consequently the required time and cost decrease while achieving a satisfactory detection rate. This article employs frequency domain method (FDM) which is applicable in discrete-event simulation models to propose a new statistic defined as the ratio of estimated signal spectrum to maximum estimated noise spectrum. The proposed method not only has the FDM advantages compared to classic screening approaches but also helps to reduce the error of associated with distinguishing important effects from unimportant ones. Furthermore, as an alternative to the existing statistics, it is shown that not only the proposed statistic does not deteriorate the power of the screening test but in some instances it helps to improve it.

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Common Random Numbers

Schruben¹

2011

Wiley Encyclopedia of Operations Research and Management Science

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By far the simplest and most widely used variance reduction technique (VRT) for stochastic simulations is simply to use the same sets of common random number streams (CRNs) to generate random variables when running simulations at different input factor settings. This is the default, or is easily implemented, in many commercial simulation software packages. However, this potentially powerful VRT should be used differently depending on how random variables are generated, how these random variables are used when running the simulation model to generate output data, and how the output data is used in analysis.

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So many factors, so little time…Simulation experiments in the frequency domain

Cited by 25 publications

References 23 publications

Sensitivity Analysis in Discrete Event Simulation Using Design of Experiments

Sensitivity Analysis in Discrete Event Simulation Using Design of Experiments

New Statistic to Increase Correctness in Simulation Factor Screening Using Frequency Domain Method

Common Random Numbers

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