Thirty years of &amp;#x201C;Batch Size Effects&amp;#x201D;

Nelson, .

doi:10.1109/wsc.2011.6147766

Cited by 2 publications

(2 citation statements)

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“…In terms of output analysis for steady-state simulation, Conway, Johnson, and Maxwell (1959) and Conway (1963) are the first papers to address the problem via the method of nonoverlapping batch means (NBM). Schmeiser (1982) makes a major contribution to the theory of NBM, as discussed by Nelson (2011) elsewhere in this Proceedings. Other important output analysis methods along the way have included regenerative analysis Iglehart 1975, Crane andLemoine 1977), standardized time series (STS) (Schruben 1983), spectral-based methods (Fishman and Kiviat 1967), and overlapping batch means (OBM) (Meketon and Schmeiser 1984), the latter of which motivates this paper.…”

Section: Introductionmentioning

confidence: 95%

Overlapping Batch Means: Something more for Nothing?

Alexopoulos

Goldsman

Wilson

2011

Proceedings of the 2011 Winter Simulation Conference (WSC)

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Output analysis methods that provide reliable point and confidence-interval estimators for system performance characteristics are critical elements of any modern simulation project. Remarkable advances in simulation output analysis have been achieved over the last thirty years, in part owing to the application of data-reuse techniques designed to improve estimator accuracy and efficiency. Many of the key insights regarding data reuse are given in the seminal 1984 Winter Simulation Conference paper by Meketon and Schmeiser that is titled "Overlapping Batch Means: Something for Nothing?" and that introduced the method of overlapping batch means (OBM). We trace the development of OBM from the original work of Meketon and Schmeiser, and we discuss some recent extensions of the method.

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Section: Introductionmentioning

confidence: 95%

Overlapping Batch Means: Something more for Nothing?

Alexopoulos

Goldsman

Wilson

2011

Proceedings of the 2011 Winter Simulation Conference (WSC)

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“…An important decision in this context is to determine the batch size m to use for variance estimation. For discussions of appropriate batch sizes to use, see Nelson (2011), Song and Schmeiser (1995) and Song (1996), to name a few. In our implementation, we set the ratio of the runlength to the batch size b = s {2} /m to 20, 50, and 110 which corresponds to m = 55, 000, 22, 000, 10, 000, respectively.…”

Section: Experiments Setupmentioning

confidence: 99%

Steady-State Co-Kriging Models

Hemmati¹

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In deterministic computer experiments, a computer code can often be run at different levels of complexity/fidelity and a hierarchy of levels of code can be obtained. The higher the fidelity and hence the computational cost, the more accurate output data can be obtained. Methods based on the co-kriging methodology Cressie (2015) for predicting the output of a high-fidelity computer code by combining data generated to varying levels of fidelity have become popular over the last two decades. For instance, Kennedy and O'Hagan (2000) first propose to build a metamodel for multi-level computer codes by using an auto-regressive model structure. Forrester et al. (2007) provide details on estimation of the model parameters and further investigate the use of co-kriging for multi-fidelity optimization based on the efficient global optimization algorithm Jones et al. (1998). Qian and Wu (2008) propose a Bayesian hierarchical modeling approach for combining lowaccuracy and high-accuracy experiments. More recently, Gratiet and Cannamela (2015) propose sequential design strategies using fast cross-validation techniques for multi-fidelity computer codes. This research intends to extend the co-kriging metamodeling methodology to study steady-state simulation experiments. First, the mathematical structure of co-kriging is extended to take into account heterogeneous simulation output variances. Next, efficient steady-state simulation experimental designs are investigated for co-kriging to achieve a high prediction accuracy for estimation of steady-state parameters. Specifically, designs consisting of replicated longer simulation runs at a few design points and replicated shorter simulation runs at a larger set of design points will be considered. Also, design with no replicated simulation runs at long simulation is studied, along with different methods for calculating the output variance in absence of replicated outputs. Stochastic co-kriging (SCK) method is applied to an M/M/1, as well as an M/M/5 queueing system. In both examples, the prediction performance of the SCK model is promising. It is also shown that the SCK method provides better response surfaces compared to the SK method. "The moving finger writes, and having written moves on. Nor all thy piety nor all thy wit, can cancel half a line of it. " Omar Khayyam iii First, I would like to thank my dear parents, for all the support and love that they have had for me through every single stage of my personal and academic life. They have always made me believe in myself and fly high. I send my gratitude to Professor Majid Jaridi, who has been beyond a professor, my "Patient Stone", making me feel the relief of having a trustworthy and caring person when I am thousands of miles away from my family. I also thank Dr Feng Yang, my advisor and Dr Xi Chen, my committee member, for being really helpful thorough my master studies at West Virginia University and being academically supportive of me through this research.

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Thirty years of “Batch Size Effects”

Cited by 2 publications

References 42 publications

Overlapping Batch Means: Something more for Nothing?

Overlapping Batch Means: Something more for Nothing?

Steady-State Co-Kriging Models

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