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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)....

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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
01 Jan 1984
TL;DR: All phases of input data analysis are covered, including data collection, choosing a modeling distribution, estimating parameters, and goodness-of-fit testing.
Abstract: This paper reviews perspectives and methods for specifying distribution and process forms and parameters from which observations are drawn to drive a stochastic simulation. All phases of input data analysis are covered, including data collection, choosing a modeling distribution, estimating parameters, and goodness-of-fit testing. A discussion is also presented concerning the debate over the desirability of fitting “standard” distributions to data as opposed to using a direct empirical distribution. Available software packages with a simulation orientation are also described.

7 citations

Proceedings ArticleDOI
08 Nov 1996
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.

5 citations

Proceedings ArticleDOI
01 Jan 1981
TL;DR: This paper questions the conventional wisdom that “standard” distributions such as the normal, gamma, or beta families should necessarily be used to model inputs for simulation and proposes an alternative, a “quasi-empirical” distribution.
Abstract: This paper questions the conventional wisdom that “standard” distributions such as the normal, gamma, or beta families should necessarily be used to model inputs for simulation and proposes an alternative. Relevant criteria are the quality of the fits and the impact of the input distributions on effective use of variance reduction techniques and on variate-generation speed. The proposed alternative, a “quasi-empirical” distribution, looks good on all these measures.

4 citations


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

  • ...…that have more than two parameters (e.g., Johnson translation distributions, phase-type distributions, Coxian distributions, generalized hyper exponential distributions), fitting can be a formidable task (see, for example, Johnson and Taffe, 1991, Johnson, 1993, and Harris and Marchal, 1997)....

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Proceedings ArticleDOI
01 Dec 1993
TL;DR: This introductory tutorial discusses the basic ideas and techniques used to obtain random variates inrete-event simulation, and how appropriate distributions should be selected to model different quantities like arrival and service times.
Abstract: Discrete-event simulation almost invariably makes uses of random quantities drawn from given probability distributions to model chance fluctuations. This introductory tutorial discusses the basic ideas and techniques used to obtain such random variates. The two main points addressed are how appropriate distributions should be selected to model different quantities like arrival and service times, and how the variate values should actually be generated from the selected distributions.

4 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). Three rather “nonclassical” classes of distributions which appear to have potential in input modeling are the Johnson translation distributions (see Storer et al, 1988), Phase-type distributions (see Johnson and Taaffe, 1991), and Bezier distributions (see Wagner and Wilson, 1996)....

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  • ...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)....

    [...]