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A Simple Numerical Approach for Infinite-State Markov Chains

Published online by Cambridge University Press:  27 July 2009

Henk C. Tijms
Affiliation:
Department of Econometrics Vrije University, 1081 HV Amsterdam, The Netherlands
Michel C. T. Van De Coevering
Affiliation:
Department of Econometrics Vrije University, 1081 HV Amsterdam, The Netherlands

Abstract

This paper presents a simple and practical approach to solving the equilibrium equations for a class of Markov chains with an infinite number of states. Markov chains arising in queueing and inventory applications often have the property that the state probabilities exhibit a geometric tail behavior. The basic idea of the approach is to reduce the infinite system of linear equations to a finite system using the geometric tail behavior of the equilibrium probabilities. The reduction typically leads to a remarkably small system of linear equations that can be routinely solved by a Gaussian elimination method. An application is given to the single-server queue with scheduled arrivals.

Type
Articles
Copyright
Copyright © Cambridge University Press 1991

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References

REFERENCES

Barker, G.P. & Plemmons, R.J. (1986). Convergent iterations for computing stationary distributions for Markov chains. SIAM Journal Algebraic Discrete Methods 7: 390398.CrossRefGoogle Scholar
Chaudry, M.L. & Templeton, J.G.C. (1983). A first course in bulk queues. New York: Wiley.Google Scholar
Everett, J.L. (1954). State probabilities in congestion problems characterized by constant holding times. Operations Research 1: 279285.Google Scholar
Fredericks, A.A. (1982). A class of approximations for the waiting time distribution in a G1/0/l queueing system. Bell System Technical Journal 61: 295325.CrossRefGoogle Scholar
Grassmann, W.K., Taksar, M.l., & Heyman, D.P. (1985). Regenerative analysis and steady- state distributions for Markov chains. Operations Research 33: 11071116.CrossRefGoogle Scholar
Neuts, M.F. (1984). Matrix-geometric solutions in stochastic models: An algorithmic approach. Baltimore: The Johns Hopkins University Press.Google Scholar
Press, W.H., Flannery, B.P., Teukoisky, S.A., & Vetterling, W.T. (1986), Numerical recipes. Cambridge, England: Cambridge University Press.Google Scholar
Ross, S. (1983). Stochastic processes. New York: Wiley.Google Scholar
Suzuki, T. (1963). Batch-arrival queueing problem. Journal of the Operations Research Society of Japan 5: 137148.Google Scholar
Takahashi, Y. & Takami, Y. (1976). A numerical method for the steady state probabilities of a G1/G/c queueing system in a general class. Journal of the Operations Research Society of Japan 19: 147157.CrossRefGoogle Scholar
Turns, H.C. (1986). Stochastic modeling and analysis: A computational approach. New York: Wiley.Google Scholar