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FINDING NON-STATIONARY STATE PROBABILITIES OF G-NETWORK WITH SIGNALS AND CUSTOMERS BATCH REMOVAL

Published online by Cambridge University Press:  03 May 2017

Mikhail Matalytski
Mikhail Matalytski*
Affiliation:
Faculty of Mathematics and Computer Science, Grodno State University, Grodno, Belarus E-mail: [email protected]
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Abstract

This paper is devoted to the research of an open Markov queueing network with positive customers and signals, and positive customers batch removal. A way of finding in a non-stationary regime time-dependent state probabilities has been proposed. The Kolmogorov system of difference-differential equations for state probabilities of such network was derived. The technique of its building, based on the use of the modified method of successive approximations combined with a series method, has been proposed. It is proved that the successive approximations converge over time to the stationary state probabilities, and the sequence of approximations converges to the unique solution of the Kolmogorov equations. Any successive approximation can be represented as a convergent power series with infinite radius of convergence, the coefficients of which satisfy the recurrence relations; that is useful for estimations. Model example illustrating the finding of time-dependent state probabilities of the network has been provided.

Keywords

applied probabilitybatch removalG-networknon-stationary state probabilitiesqueueing theorysignalssuccessive approximations
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 31 , Special Issue 4: G-Networks and their Applications , October 2017 , pp. 396 - 412
Copyright
Copyright © Cambridge University Press 2017 

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