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Proceedings ArticleDOI

Sensitivity of output performance measures to input distributions in queueing simulation modeling

01 Dec 1997-Vol. 1, pp 296-302
TL;DR: Investigation of the sensitivity of output performance measures in two types of queueing networks, namely two versions of a two-node call center, to see if network mixing might reduce the sensitivity effect.
Abstract: In Gross and Juttijudata (1997) a single node, G/G/1 queue was investigated as to the sensitivity of output performance measures, such as the mean queue wait, to the shape of the interarrival and service distributions selected. Gamma, Weibull, lognormal and Pearson type 5 distributions with identical first and second moments were investigated. Significant differences in output measures were noted for low to moderate traffic intensities (offered load, /spl rho/), in some cases, even as high as 0.8. We continue this type of investigation for two types of queueing networks, namely two versions of a two-node call center, to see if network mixing might reduce the sensitivity effect.

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Citations
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Journal ArticleDOI
TL;DR: The central idea is to transform a Gaussian vector autoregressive process into the desired multivariate time-series input process that the authors presume as having a VARTA (Vector-Autoregressive-To-Anything) distribution.
Abstract: We present a model for representing stationary multivariate time-series input processes with marginal distributions from the Johnson translation system and an autocorrelation structure specified through some finite lag. We then describe how to generate data accurately to drive computer simulations. The central idea is to transform a Gaussian vector autoregressive process into the desired multivariate time-series input process that we presume as having a VARTA (Vector-Autoregressive-To-Anything) distribution. We manipulate the autocorrelation structure of the Gaussian vector autoregressive process so that we achieve the desired autocorrelation structure for the simulation input process. We call this the correlation-matching problem and solve it by an algorithm that incorporates a numerical-search procedure and a numerical-integration technique. An illustrative example is included.

131 citations

Journal ArticleDOI
TL;DR: An automated and statistically valid algorithm is presented to fit autoregressive-to-anything (ARTA) processes with marginal distributions from the Johnson translation system to stationary univariate time-series data.
Abstract: Providing accurate and automated input-modeling support is one of the challenging problems in the application of computer simulation of stochastic systems. The models incorporated in current input-modeling software packages often fall short because they assume independent and identically distributed processes, even though dependent time-series input processes occur naturally in the simulation of many real-life systems. Therefore, this paper introduces a statistical methodology for fitting stochastic models to dependent time-series input processes. Specifically, an automated and statistically valid algorithm is presented to fit autoregressive-to-anything (ARTA) processes with marginal distributions from the Johnson translation system to stationary univariate time-series data. ARTA processes are particularly well suited to driving stochastic simulations. The use of this algorithm is illustrated with examples.

68 citations


Cites background from "Sensitivity of output performance m..."

  • ...…system might seem restrictive, it is less so than it first appears: In many applications, simulation output performance measures are insensitive to the specific input distribution chosen, provided that enough moments of the distribution are correct (see, for instance, Gross and Juttijudata 1997)....

    [...]

Journal ArticleDOI
TL;DR: This work explores the use of Bayesian statistics for verification and validation of simulation models and for simulation output analysis, in both cases using priors on the performance measures of interest.

40 citations

Journal ArticleDOI
TL;DR: Reading this paper provides readers the foundational knowledge needed to develop intuition and insights on the complexities of stochastic simple serial lines, and serves as a guide to better understand and manage the effects of variability and design factors related to improving serial production line performance.

32 citations

Proceedings ArticleDOI
01 Dec 2001
TL;DR: The authors present a general-purpose input-modeling tool for representing, fitting, and generating random variates from multivariate input processes to drive computer simulations.
Abstract: Providing accurate and automated input modeling support is one of the challenging problems in the application of computer simulation. The authors present a general-purpose input-modeling tool for representing, fitting, and generating random variates from multivariate input processes to drive computer simulations. We explain the theory underlying the suggested data fitting and data generation techniques, and demonstrate that our framework fits models accurately to both univariate and multivariate input processes.

26 citations

References
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Proceedings ArticleDOI
08 Nov 1996
TL;DR: New methods are presented for estimating univariate and bivariate Bezier distributions and a linear-programming approach is formulated that is implemented in the Windows-based software system called PRIME-PRobabilistic Input Modeling Environment.
Abstract: New methods are presented for estimating univariate and bivariate Bezier distributions. A likelihood ratio test is used to estimate the number of control points for a univariate Bezier distribution fitted to sample data. To estimate the control points of a bivariate Bezier distribution with fixed marginals based on either sample data or subjective information about the joint dependency structure, a linear-programming approach is formulated. These methods are implemented in the Windows-based software system called PRIME-PRobabilistic Input Modeling Environment. Several examples illustrate the application of these estimation procedures.

21 citations

Proceedings ArticleDOI
08 Nov 1996
TL;DR: This paper considers how bootstrapping methods can be used in selecting appropriate input models for use in a computer simulation experiment, using a goodness of-fit statistic to decide on which of several competing input models should be used.
Abstract: Bootstrap methods are a natural adjunct of computer simulation experiments; both use resampling techniques to construct the statistical distributions of quantities of interest. In this paper we consider how bootstrap methods can be used in selecting appropriate input models for use in a computer simulation experiment. The proposed method uses a goodness of-fit statistic to decide on which of several competing input models should be used. We use bootstrapping to find the distribution of the test statistic under different assumptions as to which model is the correct fit. This allows the quality of fit of the different models to be compared. The bootstrapping process can be extended to the simulation experiment itself, allowing the effect of variability of estimated parameters on the simulation output to be assessed. The methodology is described and illustrated by application to a queueing example investigating the delays experienced by motorists caused by toll booths at a bridge river crossing.

15 citations

Proceedings ArticleDOI
01 Dec 1995
TL;DR: General guidelines for selecting probabilistic input models as part of a discrete-event simulation study are presented and two short examples illustrating input modeling decisions are presented.
Abstract: General guidelines for selecting probabilistic input models as part of a discrete-event simulation study are presented. Two short examples illustrating input modeling decisions are also presented, as opposed to a complete treatment of the subject.

14 citations


"Sensitivity of output performance m..." refers background in this paper

  • ...Some general papers in this area include Fox (1981), Kelton (1984), Cheng (1993), Cheng, et al (1996), Leemis (1995, 1996) and Nelson et al (1995)....

    [...]

Proceedings ArticleDOI
01 Dec 1995
TL;DR: This tutorial describes input models that are useful when simple models are not, and how to fit these distributions to data.
Abstract: A simulation model is composed of inputs and logic; the inputs represent the uncertainty or randomness in the system, while the logic determines how the system reacts to the uncertain elements. Simple input models, consisting of independent and identically distributed sequences of random variates from standard probability distributions, are included in every commercial simulation language. Software to fit these distributions to data is also available. In this tutorial we describe input models that are useful when simple models are not.

14 citations

Proceedings ArticleDOI
01 Dec 1988
TL;DR: This work compares a number of methods for estimating the parameters of the Johnson translation family of distributions, including moment matching (MM), least squares (ordinary- (OLS), weighted- and diagonally weighted- (DWLS) least squares), and maximum likelihood (MLE).
Abstract: The Johnson translation family of distributions provides a variety of distributional shapes for the modelling of empirical data that are readily used in simulation models. We compare a number of methods for estimating the parameters of these distributions, including moment matching (MM), least squares (ordinary- (OLS), weighted- (WLS) and diagonally weighted- (DWLS) least squares), and maximum likelihood (MLE). A sampling study is made to determine the properties of the fitted parameters and estimates based on the fitted parameters, such as the quantiles of the distribution. We restrict attention to the case that the analyst knows the correct distribution when fitting the parameters.

9 citations