Due to the COVID19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 22.5 h
Q2
Teacher(s)
Aghezzaf ElHoussaine (compensates Papavasiliou Anthony); Papavasiliou Anthony;
Language
English
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 largescale 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 decisionmaking under uncertainty as mathematical programs,

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
Due to the COVID19 crisis, the information in this section is particularly likely to change.
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
Due to the COVID19 crisis, the information in this section is particularly likely to change.
 Written and/or oral 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