A general purpose module using refined descriptive sampling for installation in simulation systems. 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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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Boubalou, Meriem; Ourbih-Tari, Megdouda; Aloui, Abdelouhab; Zioui, Arezki: Comparing M/G/1 queue estimators in Monte Carlo simulation through the tested generator “getRDS” and the proposed “getLHS” using variance reduction (2019)
- Tamiti, Kenza; Ourbih-Tari, Megdouda; Aloui, Abdelouhab; Idjis, Khelidja: The use of variance reduction, relative error and bias in testing the performance of M/G/1 retrial queues estimators in Monte Carlo simulation (2018)
- Aloui, Abdelouhab; Zioui, Arezki; Ourbih-Tari, Megdouda; Alioui, Amine: A general purpose module using refined descriptive sampling for installation in simulation systems (2015)