Politecnico di Torino
Politecnico di Torino
Politecnico di Torino
Academic Year 2015/16
Discrete event models and systems
Master of science-level of the Bologna process in Computer Engineering - Torino
Teacher Status SSD Les Ex Lab Years teaching
Calafiore Giuseppe Carlo ORARIO RICEVIMENTO O2 ING-INF/04 40 40 0 5
SSD CFU Activities Area context
ING-INF/04 6 C - Affini o integrative Attivitŗ formative affini o integrative
04CFI; 01PDC; 01PDX; 01OUV
ORA-01722: invalid number
Subject fundamentals
The course is taught in Italian.

Dynamical processes arising in various contexts, such as robotics, factory automation, networks, and economical systems, do not only possess a "continuous" behavior (i.e., the one which is typically studied in classical courses on systems and control theory), but also contain a "discrete" behavior, produced by the occurrence of asynchronous "events" (for instance, a componentís failure) that may modify instantaneously the systemís state. The analysis of systems of discrete nature requires tools and models that are quite different from the ones used in the traditional study of systems with continuous states. The purpose of this course is to introduce the basic elements necessary to understand the modeling of discrete event systems, to develop the relative theory, both in a deterministic and in a stochastic setting, and to analyze quantitatively their behavior, via an analytic approach or via a computer simulation one.
Expected learning outcomes
Understanding of analytical instruments for representing discrete event dynamical systems, both in a deterministic and a stochastic setting; Learning to model simple practical problems arising from factory automation, robotics, production systems and management; Acquiring the capability of evaluating a systemís performance (analytically or via computer simulation) and of dimensioning the systemís parameters, in the design phase; Understanding the behavior of networked systems.
Prerequisites / Assumed knowledge
Basic knowledge of calculus, probability theory, and linear algebra. Some exposure of systems and control theory may be useful, although it is not strictly required as a prerequisite.
- Discrete event dynamical systems (DEDS) modeling: states, events, transitions, graphs.
- Review of probability theory and linear algebra.
- DEDS, deterministic and stochastic timed automata, formalisms.
- Computer simulation of DEDS. - Stochastic processes; Poisson, Exponential, and Gamma distributions.
- Discrete-time and continuous-time Markov chains.
- Queueing systems. - Open and closed networks of queues. Solution methods. - Network flow problems.
- Examples from applicative contexts.