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StateoftheArt Review: A User’s Guide to the Brave New World of Designing Simulation Experiments
 INFORMS Journal on Computing
, 2005
"... informs ® doi 10.1287/ijoc.1050.0136 © 2005 INFORMS Many simulation practitioners can get more from their analyses by using the statistical theory on design of experiments (DOE) developed specifically for exploring computer models. We discuss a toolkit of designs for simulators with limited DOE expe ..."
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informs ® doi 10.1287/ijoc.1050.0136 © 2005 INFORMS Many simulation practitioners can get more from their analyses by using the statistical theory on design of experiments (DOE) developed specifically for exploring computer models. We discuss a toolkit of designs for simulators with limited DOE expertise who want to select a design and an appropriate analysis for their experiments. Furthermore, we provide a research agenda listing problems in the design of simulation experiments—as opposed to realworld experiments—that require more investigation. We consider three types of practical problems: (1) developing a basic understanding of a particular simulation model or system, (2) finding robust decisions or policies as opposed to socalled optimal solutions, and (3) comparing the merits of various decisions or policies. Our discussion emphasizes aspects that are typical for simulation, such as having many more factors than in realworld experiments, and the sequential nature of the data collection. Because the same problem type may be addressed through different design types, we discuss quality attributes of designs, such as the ease of design construction, the flexibility for analysis, and efficiency considerations. Moreover, the selection of the design type depends on the metamodel (response surface) that the analysts tentatively assume; for
2005. Performance of a waveletbased spectral procedure for steadystate simulation analysis
 INFORMS Journal on Computing
, 2007
"... A summary and an analysis are given for an experimental performance evaluation of WASSP, an automated waveletbased spectral method for constructing an approximate confidence interval on the steadystate mean of a simulation output process such that the delivered confidence interval satisfies users ..."
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A summary and an analysis are given for an experimental performance evaluation of WASSP, an automated waveletbased spectral method for constructing an approximate confidence interval on the steadystate mean of a simulation output process such that the delivered confidence interval satisfies userspecified requirements on absolute or relative precision as well as coverage probability. The experimentation involved three difficult test problems, each with an output process exhibiting some combination of the following characteristics: a long warmup period, a persistent autocorrelation structure, or a highly nonnormal marginal distribution. These problems were used to compare the performance of WASSP with that of the HeidelbergerWelch algorithm and ASAP3, two sequential procedures based respectively on the methods of spectral analysis and nonoverlapping batch means. Concerning efficiency (required sample sizes) and robustness against the statistical anomalies commonly encountered in simulation studies, WASSP outperformed the HeidelbergerWelch procedure and compared favorably with ASAP3. Key words: simulation, statistical analysis; spectral analysis; steadystate analysis; wavelet analysis
2011b. “Skart: A Skewness and AutoregressionAdjusted Batch Means Procedure for Simulation Analysis
 IIE Transactions
"... We discuss Skart, an automated batchmeans procedure for constructing a skewness and autoregressionadjusted confidence interval for the steadystate mean of a simulation output process. Skart is a sequential procedure designed to deliver a confidence interval that satisfies userspecified requir ..."
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We discuss Skart, an automated batchmeans procedure for constructing a skewness and autoregressionadjusted confidence interval for the steadystate mean of a simulation output process. Skart is a sequential procedure designed to deliver a confidence interval that satisfies userspecified requirements concerning not only coverage probability but also the absolute or relative precision provided by the halflength. Skart exploits separate adjustments to the halflength of the classical batchmeans confidence interval so as to account for the effects on the distribution of the underlying Student’s tstatistic that arise from nonnormality and autocorrelation of the batch means. Skart also delivers a point estimator for the steadystate mean that is approximately free of initialization bias. In an experimental performance evaluation involving a wide range of test processes, Skart compared favorably with other simulation analysis methods—namely, its predecessors ASAP3, WASSP, and SBatch as well as ABATCH, LBATCH, the HeidelbergerWelch procedure, and the LawCarson procedure.
QPME  A Performance Modeling Tool Based on Queueing Petri Nets
 ACM SIGMETRICS PERFORMANCE EVALUATION REVIEW (PER), SPECIAL ISSUE ON TOOLS
, 2009
"... Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. ..."
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Cited by 7 (4 self)
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Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. In this paper, we present QPME (Queueing Petri net Modeling Environment) a tool that supports the modeling and analysis of systems using queueing Petri nets. QPME provides an Eclipsebased editor for designing queueing Petri net models and a powerful simulation engine for analyzing the models. After presenting the tool, we discuss the ongoing work on the QPME project and the planned future enhancements of the tool.
A distributionfree tabular CUSUM chart for autocorrelated data
 IIE Transactions
, 2007
"... A distributionfree tabular CUSUM chart is designed to detect shifts in the mean of an autocorrelated process. The chart’s average run length (ARL) is approximated by generalizing Siegmund’s ARL approximation for the conventional tabular CUSUM chart based on independent and identically distributed ..."
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Cited by 6 (4 self)
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A distributionfree tabular CUSUM chart is designed to detect shifts in the mean of an autocorrelated process. The chart’s average run length (ARL) is approximated by generalizing Siegmund’s ARL approximation for the conventional tabular CUSUM chart based on independent and identically distributed normal observations. Control limits for the new chart are computed from the generalized ARL approximation. Also discussed are the choice of reference value and the use of batch means to handle highly correlated processes. The new chart is compared with other distributionfree procedures using stationary test processes with both normal and nonnormal marginals.
A Procedure for Generating BatchMeans Confidence Intervals for Simulation: Checking Independence and Normality
, 2007
"... Batch means are sample means of subsets of consecutive subsamples from a simulation output sequence. Independent and normally distributed batch means are not only the requirement for constructing a confidence interval for the mean of the steadystate distribution of a stochastic process, but are als ..."
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Batch means are sample means of subsets of consecutive subsamples from a simulation output sequence. Independent and normally distributed batch means are not only the requirement for constructing a confidence interval for the mean of the steadystate distribution of a stochastic process, but are also the prerequisite for other simulation procedures such as ranking and selection (R&S). We propose a procedure to generate approximately independent and normally distributed batch means, as determined by the von Neumman test of independence and the chisquare test of normality, and then to construct a confidence interval for the mean of a steadystate expected simulation response. It is our intention for the batch means to play the role of the independent and identically normally distributed observations that confidence intervals and the original versions of R&S procedures require. We perform an empirical study for several stochastic processes to evaluate the performance of the procedure and to investigate the problem of determining valid batch sizes.
ConfidenceInterval Estimation Using QuasiIndependent Sequences
 IIE Transactions. To Appear
"... A quasiindependent (QI) subsequence is a subset of timeseries observations obtained by systematic sampling. Because the observations appear to be independent, as determined by the runs tests, classical statistical techniques can be used on those observations directly. This paper discusses implemen ..."
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A quasiindependent (QI) subsequence is a subset of timeseries observations obtained by systematic sampling. Because the observations appear to be independent, as determined by the runs tests, classical statistical techniques can be used on those observations directly. This paper discusses implementation of a sequential procedure to determine the simulation run length to obtain a QI subsequence, and the batch size for constructing confidence intervals for an estimator of the steadystate mean of a stochastic process. Our QI procedure increases the simulation run length and batch size progressively until a certain number of essentially independent and identically distributed samples are obtained. The only (mild) assumption is that the correlations of the stochastic process output sequence eventually die off as the lag increases. An experimental performance evaluation demonstrates the validity of the QI procedure. 1.
QPME 2.0  A Tool for Stochastic Modeling and Analysis using Queueing Petri Nets
"... Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. ..."
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Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. In this paper, we present Version 2.0 of our tool QPME (Queueing Petri net Modeling Environment) for modeling and analysis of systems using queueing Petri nets. The development of the tool was initiated by Samuel Kounev in 2003 at the Technische Universität Darmstadt in the group of Prof. Alejandro Buchmann. Since then the tool has been distributed to more than 100 organizations worldwide. QPME provides an Eclipsebased editor for building queueing Petri net models and a powerful simulation engine for analyzing the models. After presenting the tool, we discuss ongoing work on the QPME project and the planned future enhancements of the tool.
On the Use of Queueing Petri Nets for Modeling and Performance Analysis of Distributed Systems
 CHAPTER IN VEDRAN KORDIC (ED.) PETRI NET, THEORY AND APPLICATION. ADVANCED ROBOTIC SYSTEMS INTERNATIONAL
, 2007
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Performance of Skart: A Skewness and AutoregressionAdjusted Batch Means Procedure for Simulation Analysis
, 2010
"... informs doi 10.1287/ijoc.1100.0401 ..."
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