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A general purpose module using refined descriptive sampling for installation in simulation systems

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Abstract

Some form of random sampling is employed in all simulation studies, so, some possibly substantial sampling errors are inevitable. Therefore, a new paradigm emerged: it is not always necessary to resort to randomness to generate inputs. Then, novel sampling methods were derived from this paradigm like for example Refined Descriptive Sampling (RDS). In this paper, we propose a software component under Linux called getRDS which implements an RDS number generator of high quality using the RDS method. It was highly tested by statistical tests and compared to the well known Mersenne Twister random number generator MT19937 (MT). We noticed that getRDS has passed better all tests than MT. Some illustrations of the uniformity are also given together with its comparison with MT through an M/M/1 simulation system. The obtained results through simulation demonstrate that the software component produces an accurate point estimates of the true parameters. Moreover, the getRDS random number generator can significantly improve the performance of the M/M/1 queues compared to MT since its variance reduction is over 50 % in some cases.

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Notes

  1. http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c.

  2. Toolbox for pseudo and quasi random number generation and RNG tests available in http://cran.univ-lyon1.fr/web/packages/randtoolbox/

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Authors and Affiliations

  1. Laboratory LIMED, Faculty of Exacts Sciences, University of Bejaia, Bejaia, Algeria

    Abdelouhab Aloui

  2. Laboratory of Applied Mathematics, Faculty of Exacts Sciences, University of Bejaia, Bejaia, Algeria

    Arezki Zioui & Megdouda Ourbih-Tari

  3. University A. MIRA of Bejaia, Bejaia, Algeria

    Amine Alioui

Authors
  1. Abdelouhab Aloui
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  2. Arezki Zioui
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  3. Megdouda Ourbih-Tari
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  4. Amine Alioui
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Correspondence to Abdelouhab Aloui.

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Aloui, A., Zioui, A., Ourbih-Tari, M. et al. A general purpose module using refined descriptive sampling for installation in simulation systems. Comput Stat 30, 477–490 (2015). https://doi.org/10.1007/s00180-014-0545-7

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  • DOI: https://doi.org/10.1007/s00180-014-0545-7

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