Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-11T14:42:25.887Z Has data issue: false hasContentIssue false

ON THE TIME-DEPENDENT BEHAVIOR OF A MARKOVIAN REENTRANT-LINE MODEL

Published online by Cambridge University Press:  03 August 2018

Brian Fralix*
Affiliation:
Department of Mathematical Sciences Clemson University, Clemson, SC, USA E-mail: bfralix@clemson.edu

Abstract

We use the random-product technique from [5] to study both the steady-state and time-dependent behavior of a Markovian reentrant-line model, which is a generalization of the preemptive reentrant-line model studied in the work of Adan and Weiss [2]. Our results/observations yield additional insight into why the stationary distribution of the reentrant-line model from [2] exhibits an almost-geometric product-form structure: indeed, our generalized reentrant-line model, when stable, admits a stationary distribution with a similar product-form representation as well. Not only that, the Laplace transforms of the transition functions of our reentrant-line model also have a product-form structure if it is further assumed that both Buffers 2 and 3 are empty at time zero.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Abate, J. & Whitt, W. (1995). Numerical inversion of Laplace transforms of probability distributions. ORSA Journal of Computing 7: 3643.Google Scholar
2.Adan, I. & Weiss, G. (2006). Analysis of a simple Markovian re-entrant line with infinite supply of work under the LBFS policy. Queueing Systems 54: 169183.Google Scholar
3.Adan, I., van Leeuwaarden, J. & Selen, J. (2017). Analysis of Structured Markov Chains. Draft available at https://arxiv.org/abs/1709.09060.Google Scholar
4.Brown, J.W. & Churchill, R.V. (2009). Complex variables and applications, 8th ed. New York: McGraw-Hill Companies.Google Scholar
5.Buckingham, P. & Fralix, B. (2015). Some new insights into Kolmogorov's criterion, with applications to hysteretic queues. Markov Processes and Related Fields 21: 339368.Google Scholar
6.Doroudi, S., Fralix, B. & Harchol-Balter, M. (2016). Clearing Analysis on Phases: exact limiting probabilities for skip-free, unidirectional, quasi-birth-death processees. Stochastic Systems 6: 420458.Google Scholar
7.Fralix, B. (2015). When are two Markov chains similar? Statistics and Probability Letters 107: 199203.Google Scholar
8.Fralix, B. (2018). A new look at a smart polling model. Mathematical Methods of Operations Research, to appear.Google Scholar
9.Gelenbe, E. (1991). Product-form queueing networks with negative and positive customers. Journal of Applied Probability 28: 656663.Google Scholar
10.Joyner, J. & Fralix, B. (2015). A new look at Markov processes of G/M/1-type. Stochastic Models 32: 253274.Google Scholar
11.Joyner, J. & Fralix, B. (2017). A new look at block-structured Markov processes. Under revision: a draft of this paper can be accessed at http://bfralix.people.clemson.edu/preprints.htm.Google Scholar
12.Latouche, G. & Ramaswami, V. (1999). Introduction to matrix-analytic methods in stochastic modeling. Philadelphia: ASA-SIAM Publications.Google Scholar
13.Liu, X. & Fralix, B. (2017). New applications of lattice-path counting to Markovian queues. Submitted for publication: a draft of this work can be downloaded from http://bfralix.people.clemson.edu/preprints.htm.Google Scholar
14.Selen, J. & Fralix, B. (2017). Time-dependent analysis of a multi-server priority system. Queueing Systems 87: 379415.Google Scholar