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Modelling and Optimization


At the end of this module, the student will have understood and be able to explain (main concepts) :
- Various approaches to analyse and evaluate the performance of discrete event
system DES,
- Various types of modelling for these systems (deterministic or
stochastic models, numerical and combinatorial optimisation models, models of
- Algorithms to solve these problems.
The student will be able to:
Model and solve operational research problems (optimisation, graphs, stochastic
process) and discrete-event systems problems.
Model stochastic systems, such as a network of queues using Markov chains,
compute the stationary measures, and compute its capacity.
Model a DES with Petri nets, analyse the properties of the Petri net using various methods
of analysis (exhaustive and structural).

Needed prerequisite

Linear Algebra, Probabilities, Dynamic systems, Basic concepts in logics and in Petri Nets.

Form of assessment

The evaluation of outcome prior learning is made as a continuous training during the semester. According ot the teaching, the assessment will be different: as a written exam, an oral exam, a record, a written report, peers review...