Comparing M/G/1 queue estimators in Monte Carlo simulation through the tested generator “getRDS” and the proposed “getLHS” using variance reduction
-
Meriem Boubalou
,
Megdouda Ourbih-Tari
Abstract
In this paper, we propose a Latin hypercube sampling (LHS) number generator in C language under Linux called getLHS in order to compare both methods LHS and refined descriptive sampling (RDS) method. It was highly tested by adequate statistical tests and compared statistically to the getRDS number generator. We noticed that getRDS has passed all tests better than the proposed getLHS generator. A simulation of M/G/1 queues is performed using getRDS to sample inputs from the RDS method and getLHS to sample inputs from the LHS method. The results obtained through simulation demonstrate that the RDS method produces more accurate point estimates of the true parameters than the LHS method. Moreover, the RDS method can significantly improve the performance of the studied queues compared to the well-known LHS method since its variance reduction factor is quite good in almost all cases. It is then proved that RDS is an improvement over LHS at least on queues.
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© 2019 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- A third-order weak approximation of multidimensional Itô stochastic differential equations
- Particle diffusion Monte Carlo (PDMC)
- Random walk on rectangles and parallelepipeds algorithm for solving transient anisotropic drift-diffusion-reaction problems
- Analysis of a non-Markovian queueing model: Bayesian statistics and MCMC methods
- On solving stochastic differential equations
- On the sample-mean method for computing hyper-volumes
- Comparing M/G/1 queue estimators in Monte Carlo simulation through the tested generator “getRDS” and the proposed “getLHS” using variance reduction
Articles in the same Issue
- Frontmatter
- A third-order weak approximation of multidimensional Itô stochastic differential equations
- Particle diffusion Monte Carlo (PDMC)
- Random walk on rectangles and parallelepipeds algorithm for solving transient anisotropic drift-diffusion-reaction problems
- Analysis of a non-Markovian queueing model: Bayesian statistics and MCMC methods
- On solving stochastic differential equations
- On the sample-mean method for computing hyper-volumes
- Comparing M/G/1 queue estimators in Monte Carlo simulation through the tested generator “getRDS” and the proposed “getLHS” using variance reduction
