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Soft Computing
Published in: Soft Computing 11/2009

01-09-2009 | Original Paper

Monte Carlo methods in fuzzy queuing theory

Authors: Areeg Abdalla, James J. Buckley

Published in: Soft Computing | Issue 11/2009

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Abstract

In this paper, we consider an optimization problem in fuzzy queuing theory that was first used in web planning. This fuzzy optimization problem has no solution algorithm and approximate solutions were first produced by computing the fuzzy value of the objective function for only sixteen values of the fuzzy variables. We introduce our fuzzy Monte Carlo method, using a quasi-random number generator, to produce 100,000 random sequences of fuzzy vectors for the fuzzy variables, which will present a much better approximate solution.
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Literature
Buckley JJ (2003) Fuzzy probabilities: new approach and applications. Springer, HeidelbergMATH
Buckley JJ (2004) Fuzzy probabilities and fuzzy sets for web planning. Springer, HeidelbergMATH
Buckley JJ, Eslami E, Feuring T (2002) Fuzzy mathematics in eonomics and engineering. Physica, HeidelbergMATH
Buckley JJ, Feuring T, Hayashi Y (2001) Fuzzy queuing theory revisited. Int J Uncertain Fuzz Knowl Based Syst 9:527–538MATHMathSciNet
Henderson SG, Chiera BA, Cooke RM (2000) Generating “dependent” quasi-random numbers. In: Proceedings of the 2000 winter simulation conference, Orlando, FL, December 10–13, pp 527–536
Menasce DA, Almeida VAF (1998) Capacity planning for web performance. Prentice-Hall, Upper Saddle River
Press WH, Flannery BP, Teukolsky SA, Vetterling WT (2002) Numerical recipes in C: the art of scientific computing, 2nd edn. Cambridge University Press, London
Taha HA (1992) Operations research, 5th edn. Macmillan, New YorkMATH
Metadata
Title
Monte Carlo methods in fuzzy queuing theory
Authors
Areeg Abdalla
James J. Buckley
Publication date
01-09-2009
Publisher
Springer-Verlag
Published in
Soft Computing / Issue 11/2009
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-008-0376-y

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