5 credits
30.0 h + 22.5 h
Q2
Teacher(s)
Chevalier Philippe;
Language
English
Main themes
Introduction to stochastic models in operations research. Study of renewal processes, Markov chains, Markov Processes, Markov Decision Processes. Applications to inventory models, queuing models, branching processes, random walks, etc.
Content
- Poisson processes and their properties
- Markov chains with a finite number of states
- Renewal processes and stopping rules
- Markov chains with an infinite number of states
- The notion of reveribility
- Markov processes
- Birth and death processes
- Queueing theory and networks of queues
- Fluid models for queues
- Various applications, such as inventory management, replacement, reliability and job shop modeling.
Teaching methods
The course consists in weekly lectures and 11 exercice sessions. One of the courses will be devoted to the student presentations of their simulation projects and another session will host a practioner to present a real world application of the course contents.
Evaluation methods
Students will be evaluated through a written exam based on the objectives of the course. The exam consists in exercices applying the concepts viewed in the course. Many examples of questions of previous exams are solved during the exercice sessions.
The students will have to build a simulation model in order to analyse and understand the behavior of a congested stochastic system. This assignment is done in groups.
The students will have to build a simulation model in order to analyse and understand the behavior of a congested stochastic system. This assignment is done in groups.
Online resources
Bibliography
Lecture recommandée : livre "Stochastic Processes: Theory for applications" de R. Gallagher, 2013, disponible en ligne : http://www.rle.mit.edu/rgallager/notes.htm
Faculty or entity
MAP
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering
Master [120] in Mathematical Engineering
Master [120] in Computer Science and Engineering
Master [120] in Computer Science
Master [120] in data Science: Information technology