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Download SOR2321 – QUEUING THEORY AND MARKOV CHAINS Queuing Lecturer:
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SOR2321 – QUEUING THEORY AND MARKOV CHAINS Lecturer: Dr. J. Sklenar Credits: 5 ECTS Prerequisites: Pure or Applied Mathematics Co- Prerequisites: SOR1201 Lectures: Introductory lectures and tutorials will be given, but most of the work will be carried on individually. Semester/s: 1 Queuing Introduction to Queuing Theory, Birth-and-Death Processes Poisson Process and M/M/1 Model in Detail Other Markovian Models: Multichannel, Limited Capacity and Population Summary of Results for Other Single Queue Models Open and Closed Jackson Networks Simulation Techniques in Queueing Theory Markov Chains Definition of Markov chains within the context of Stochastic Processes Transition probability matrices, Chapman-Kolmogorov theorem Classification of states Asymptotic results for irreducible, recurrent, aperiodic Markov chains Stationary distributions Simulation and Monte Carlo Introduction to Continuous-time Markov chains Applications mainly from computer science will be discussed throughout the course. Method of Assessment: 100% Examination (2 hours duration) Suggested Texts: Gross, D., Harris, C.M. (2008) Fundamentals of Queueing Theory, John Wiley & Sons. Tijms, H.C. (2003) A First Course in Stochastic Models, John Wiley & Sons. Prabhu, N.U. (1997) Foundations of Queueing Theory, International Series in OR & Management Sciences. Allen, A.O. (2009) Probability, Statistics and Queueing Theory, Academic. Banks, J., Carson, J.S., Nelson, B. and Nicol, D. (2004) Discrete Event Simulation, Prentice-Hall Inc. Kelton, W.D. (2006) Simulation with Arena, McGraw-Hill. Belch G., Greiner S., de Meer H., Trivedi K.S., (2006) Queueing Networks and Markov ChainsModeling and Performance Evaluation with Computer Science Applications, Wiley Stewart, W. (2009) Probability, Markov Chains, Queues, and Simulation, Princton University Press Haggstrom O., (2002) Finite Markov Chains And Algorithmic Applications , Cambridge University Press Stirzaker D., (2005) Stochastic Processes and Models, Oxford University Press Dept. of Statistics & Operations Research Page 59 of 59
