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Unit information: Introduction to Queueing Networks in 2018/19

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Unit name Introduction to Queueing Networks
Unit code MATH35800
Credit points 10
Level of study H/6
Teaching block(s) Teaching Block 1B (weeks 7 - 12)
Unit director Dr. Ayalvadi Ganesh
Open unit status Not open

Math 21400 Probability 2



School/department School of Mathematics
Faculty Faculty of Science


Unit aims

To introduce stochastic models for the description and analysis of simple queues and queueing networks.

General Description of the Unit

Queues are a fact of life - in banks, supermarkets, health care, traffic etc. The modelling and evaluation of individual queueing systems (in terms of quantities such as customer arrival patterns, service demands, scheduling priorities for different customer classes, queue size and waiting times) has been a rich source of theory and application in applied probability and operational research. Networks of linked queueing systems have gained wide popularity for modelling and performance-evaluation in telecommunications, computer systems and manufacturing.

The course will introduce relevant concepts in the context of a single server queue before going on to develop models and performance criteria applicable to more general networks. Applications will be explored through homework problems.

Relation to Other Units

The units Information Theory, Financial Mathematics, Queuing Networks and Complex Networks apply probabilistic methods, learnt in Applied Probability 2, to problems arising in various fields.

Additional unit information can be found at

Intended learning outcomes

Students who successfully complete this unit should be able to:

  • identify the transition rates for simple Markov processes from an informal description of the system;
  • construct Markov process models of simple queueing networks, specified in terms of the transition rates, and understand the basic properties of such models;

define the concepts of reversed and reversible Markov processes and use them to *construct equilibrium distributions for simple queueing networks; compute the distribution of the queue size as seen in equilibrium by arrivals and departures;

  • use Little's theorem to compute appropriate performance measures for simple systems.

Transferable Skills

The ability to translate practical problems into mathematics and the construction of appropriate probabilistic models.

Teaching details

Lectures and weekly problem sheets, from which work will be set and marked, with outline solutions handed out a fortnight later.

Assessment Details

100% Examination

Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.

Reading and References

Reading and references are available at