Discrete Event Systems

Recommended Prerequisites

Theory of Computation, Statistics.

Teaching Methods

Teaching is organised as two complementary components, theory and practice. Lectures (T) are mainly of an expository nature, but are also used to answer questions of general interest to the class. Practical (TP) sessions serve to consolidate the concepts presented in the lectures through modelling and analysis exercises, both on paper and on the computer. A problem-solving assignment involving the modelling and analysis of simplified, but realistic, systems using suitable modelling software is also proposed and assessed.

Learning Outcomes

This course unit focuses on modelling and analysis of discrete event systems, with application to the four main areas of the programme. Students should become able to model and analyse simple traffic, manufacturing, computer (hardware and software) and communication systems, among others, as discrete event systems.

In addition, students should develop skills in analysis and synthesis, problem solving, critical reasoning and self-learning, as well as the ability to apply the theoretical knowledge acquired to concrete practical settings.

Work Placement(s)

No

Syllabus

1. Systems and models: concepts; types of systems; DES and application examples.
2. Finite-state automata: language models of DES; analysis of DES based on finite-state automata.
3. Petri nets: definition; comparison with automata; analysis of Petri nets.
4. Timed automata and timed Petri nets.
5. Stochastic timed automata.
6. Markov chains.
7. Introduction to queueing theory.

Assessment Methods

Assessment
Resolution Problems: 20.0%
Exam: 40.0%
Frequency: 40.0%

Bibliography

Christos G. Cassandras and Stéphane Lafortune, Introduction to Discrete Event Systems, Springer, 2007.

 Branislav Hrúz and MengChu Zhou, Modeling and Control of Discrete-Event Dynamic Systems: with Petri Nets and Other Tools, Springer, 2007.

 Armin Zimmermann, Stochastic Discrete Event Systems: Modeling, Evaluation, Applications, Springer, 2007

 Wai-Ki Ching and Michael K. Ng, Markov Chains: Models, Algorithms and Applications, Springer, 2006.