Monte Carlo Simulation Models Evolving in Replicated Runs: A Methodology to Choose the Optimal Experimental Sample Size

The idea of a methodology capable of determining in a precise and practical way the optimal sample size came from studying Monte Carlo simulation models concerning financial problems, risk analysis, and supply chain forecasting. In these cases the number of extractions from the frequency distributio...

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Bibliographic Details
Published in:Mathematical Problems in Engineering 2012-01, Vol.2012 (2012), p.1246-1262-271
Main Authors: Cassettari, Lucia, Revetria, Roberto, Mosca, Roberto
Format: Article
Language:English
Subjects:
Computer simulation
Cost analysis
Factorial experiments
Frequency distribution
Mathematical analysis
Mathematical models
Mathematical problems
Methodology
Monte Carlo methods
Monte Carlo simulation
Random variables
Risk analysis
Sample size
Samples
Simulation models
Standard deviation
Statistical analysis
Statistical methods
Supply chains
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Summary:The idea of a methodology capable of determining in a precise and practical way the optimal sample size came from studying Monte Carlo simulation models concerning financial problems, risk analysis, and supply chain forecasting. In these cases the number of extractions from the frequency distributions characterizing the model is inadequate or limited to just one, so it is necessary to replicate simulation runs many times in order to obtain a complete statistical description of the model variables. Generally, as shown in the literature, the sample size is fixed by the experimenter based on empirical assumptions without considering the impact on result accuracy in terms of tolerance interval. In this paper, the authors propose a methodology by means of which it is possible to graphically highlight the evolution of experimental error variance as a function of the sample size. Therefore, the experimenter can choose the best ratio between the experimental cost and the expected results.
ISSN:1024-123X
1563-5147
DOI:10.1155/2012/463873