Parallélisation d'une Combinaison des Méthodes de Monte-Carlo et Quasi-Monte-Carlo et Application aux Réseaux de Files d'Attente

Bruno Tuffin; Louis-Marie Le Ny

RAIRO - Operations Research (2010)

  • Volume: 34, Issue: 1, page 85-98
  • ISSN: 0399-0559

Abstract

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We propose a parallel algorithm which uses both Monte-Carlo and quasi-Monte-Carlo methods. A detailed analysis of this algorithm, followed by examples, shows that the estimator's efficiency is a linear function of the processor number. As a concrete application example, we evaluate performance measures of a multi-class queueing network in steady state.

How to cite

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Tuffin, Bruno, and Le Ny, Louis-Marie. "Parallélisation d'une Combinaison des Méthodes de Monte-Carlo et Quasi-Monte-Carlo et Application aux Réseaux de Files d'Attente." RAIRO - Operations Research 34.1 (2010): 85-98. <http://eudml.org/doc/197810>.

@article{Tuffin2010,
abstract = { We propose a parallel algorithm which uses both Monte-Carlo and quasi-Monte-Carlo methods. A detailed analysis of this algorithm, followed by examples, shows that the estimator's efficiency is a linear function of the processor number. As a concrete application example, we evaluate performance measures of a multi-class queueing network in steady state. },
author = {Tuffin, Bruno, Le Ny, Louis-Marie},
journal = {RAIRO - Operations Research},
keywords = {Branch and bound; multi-processor flow-shop; m-machine problems; inputs and selection.; Monte-Carlo; quasi-Monte-Carlo; parallel simulation; queueing networks; normalization constant},
language = {eng},
month = {3},
number = {1},
pages = {85-98},
publisher = {EDP Sciences},
title = {Parallélisation d'une Combinaison des Méthodes de Monte-Carlo et Quasi-Monte-Carlo et Application aux Réseaux de Files d'Attente},
url = {http://eudml.org/doc/197810},
volume = {34},
year = {2010},
}

TY - JOUR
AU - Tuffin, Bruno
AU - Le Ny, Louis-Marie
TI - Parallélisation d'une Combinaison des Méthodes de Monte-Carlo et Quasi-Monte-Carlo et Application aux Réseaux de Files d'Attente
JO - RAIRO - Operations Research
DA - 2010/3//
PB - EDP Sciences
VL - 34
IS - 1
SP - 85
EP - 98
AB - We propose a parallel algorithm which uses both Monte-Carlo and quasi-Monte-Carlo methods. A detailed analysis of this algorithm, followed by examples, shows that the estimator's efficiency is a linear function of the processor number. As a concrete application example, we evaluate performance measures of a multi-class queueing network in steady state.
LA - eng
KW - Branch and bound; multi-processor flow-shop; m-machine problems; inputs and selection.; Monte-Carlo; quasi-Monte-Carlo; parallel simulation; queueing networks; normalization constant
UR - http://eudml.org/doc/197810
ER -

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