Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 22.5 h
Q2
Teacher(s)
Aghezzaf El-Houssaine (compensates Papavasiliou Anthony); Papavasiliou Anthony;
Language
English
Prerequisites
· A course in linear, non-linear, and integer programming.
· An introductory course to probability theory: probability space, probability, random variable, mathematical expectation, independence, law of large numbers, '.
· Knowledge of a mathematical programming language (AMPL, Matlab, OPL-Studio, ...)
· An introductory course to probability theory: probability space, probability, random variable, mathematical expectation, independence, law of large numbers, '.
· Knowledge of a mathematical programming language (AMPL, Matlab, OPL-Studio, ...)
Main themes
How to formulate an optimization problem in which data are prone to uncertainty? How to take into account disclosed information and revealed values of the parameters during the stages of the optimization process? How to solve the optimization models thus obtained? Stochastic optimization is the ideal framework for dealing with such issues. Various solution techniques for large-scale problems will also be discussed: Benders decomposition, Nested Bendersdecomposition, Lagrangian methods, ... Applications: Production, logistics, finance, ...
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
· Formulate problems of decision-making under uncertainty as mathematical programs,
|
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
- Mathematical background (duality, probability theory)
- Stochastic programming models
- Value of perfect information and the value of the stochastic solution
- Cutting plane algorithms
- Dynamic programming
- Stochastic dual dynamic programming
- Lagrange relaxation
Teaching methods
2 hours of magistral courses per week, and 2 hours of training sessions per week. Homeworks will be evaluated by the instructor and/or the teaching assistant.
Evaluation methods
- Written exam
- Regular homework assignments
Online resources
Bibliography
- Notes de cours
- Impressions de manuels ou articles fournies au cours. Le livre suivant servira de support pour la plupart du cours : John Birge, Francois Louveaux, "Introduction to Stochastic Programming"
Faculty or entity
MAP
