Operational Research

linma2491  2020-2021  Louvain-la-Neuve

Operational Research
Due to the COVID-19 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
Aghezzaf El-Houssaine (compensates Papavasiliou Anthony); Papavasiliou Anthony;
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, ...

At the end of this learning unit, the student is able to :

1 ·         Formulate problems of decision-making under uncertainty as mathematical programs,
  • Identify mathematical structures in large-scale mathematical programs that enable their decomposition,
  • Design algorithms for solving large-scale optimization problems under uncertainty,
  • Implement algorithms for solving large-scale stochastic optimization problems,
  • Evaluate the quality of alternative policies for problems of decision-making under uncertainty
  • 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 COVID-19 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 COVID-19 crisis, the information in this section is particularly likely to change.

  • Written and/or oral exam
  • Regular homework assignments
  • 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

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Master [120] in Mathematical Engineering

Master [120] in Data Science Engineering

Master [120] in Data Science: Information Technology