Comparing M/G/1 queue estimators in Monte Carlo simulation through the tested generator “getRDS” and the proposed “getLHS” using variance reduction

Author:

Boubalou Meriem,Ourbih-Tari Megdouda,Aloui Abdelouhab,Zioui Arezki

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Statistics and Probability

Reference46 articles.

1. Implementing refined descriptive sampling into three-phase discrete-event simulation models;Comm. Statist. Simulation Comput.,2017

2. The use of variance reduction, relative error and bias in testing the performance of M/G/1\rm M/G/1 retrial queues estimators in Monte Carlo simulation;Monte Carlo Methods Appl.,2018

3. The Threshold Policy in an M/G/1 Queue with an Exceptional First Vacation;Inform. Syst. Oper. Res.,1998

4. A general purpose module using refined descriptive sampling for installation in simulation systems;Comput. Statist.,2015

5. A hybrid kinetic-thermodynamic Monte Carlo model for simulation of homogeneous burst nucleation;Monte Carlo Methods Appl.,2018

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3