Stochastic programming

Crédit : 3 ECTS
Langue du cours : anglais

Volume horaire

• CM : 15 h
• Volume horaire global (hors stage) : 15 h

Compétences à acquérir

- Identify the main stochastic programming models - Understand the scenario formulation in stochastic programming. - Formulate a problem as a multistage stochastic program.

Description du contenu de l'enseignement

Uncertainties are ubiquitous in modeling real-world problems. Including uncertainty in an optimization model is now standard practice in industry, thanks to the development of both mathematical models and efficient software. In this course, we will discuss several classes of optimization problems that account for uncertainty in the problem data. The concepts of multistage problems, probabilistic constraints and risk measures will be used to derive the problem formulations of interest. We will also review algorithms that can be used to tackle stochastic programming problems, from both a theoretical and a practical perspective using recently developed packages.

Mode de contrôle des connaissances

Written exam

Pré-requis recommandés

Basics of linear programming.

Pré-requis obligatoires

Basics of matrix linear algebra and statistics.

Bibliographie, lectures recommandées

• M. Biel and M. Johansson, Efficient stochastic programming in Julia, INFORMS Journal of Computing (2022)
• J. R. Birge and F. Louveaux, Introduction to Stochastic Programming 2nd Edition, Springer (2011)
• A. Shapiro, D. Dentcheva and A. Ruszczynski, Lectures on Stochastic Programming, 3rd edition, SIAM (2021)

Enseignant responsable

CLEMENT ROYER