Simulating Stable Stochastic Systems, II: Markov Chains

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Simulating Stable Stochastic Systems, II: Markov Chains

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Journal of the ACM (JACM) archive
Volume 21 , Issue 1 (January 1974) table of contents

Pages: 114 - 123

Year of Publication: 1974

ISSN:0004-5411

Authors

Michael A. Crane	Control Analysis Corporation, 800 Welch Rd., Palo Alto, California
Donald L. Iglehart	Department of Operations Reserach, Stanford University, Stanford, California

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 3, Downloads (12 Months): 35, Citation Count: 29

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ABSTRACT

A technique for simulating GI/G/s queues is shown to apply to simulations of discrete and continuous-time Markov chains. It is possible to address questions of simulation run duration and of starting and stopping simulations because of the existence of a random grouping of observations which produces independent identically distributed blocks from the start of the simulation. This grouping allows confidence intervals to be obtained for a general function of the steady-state distribution of the Markov chain. The technique is illustrated with simulation of an (s, S) inventory model in discrete time and the classical repairman problem in continuous time. Consideration is also given to determining system sensitivity to errors and uncertainty in the input parameters.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	CHUNG, K.L. Markov Chaius with Stationary Transition Probabilities. Springer, Berlin, 1960.
	2	CuuNa, K. L. A Course in Probability. Harcourt, Brace and World, New York, 1968.
	3	Michael A. Crane , Donald L. Iglehart, Simulating Stable Stochastic Systems, I: General Multiserver Queues, Journal of the ACM (JACM), v.21 n.1, p.103-113, Jan. 1974 [doi>10.1145/321796.321805]
	4	FISRMAN, G., AND K~WA% P. The analysis of simulation generated time series. Management Sci. 13 (1967), 525-557.
	5	WAGN~a, H. Principles of Operations Research. Prentice-Hall, Englewood Cliffs, N. J., 1969.

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