Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 463873, 17 pages
Research Article

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

DIPTEM, University of Genoa, 16145 Genova, Italy

Received 1 October 2011; Revised 29 February 2012; Accepted 17 March 2012

Academic Editor: Kwok W. Wong

Copyright © 2012 Lucia Cassettari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.