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Optim under uncertainty

Optimization under uncertainty#

Overview#

The objective of this course is to present different techniques to handle uncertainty in decision problems. These techniques will be illustrated on several applications e.g. inventory control, scheduling, energy, machine learning.

Syllabus : Introduction to uncertainty in optimization problems; Reminders (probability, dynamic programming, ...); Markov chains; Markov decision processes; Stochastic programming; Robust optimization.

https://moodle.caseine.org/course/view.php?id=1084

In brief#

  • Period: semester 9
  • Credits: 6 ECTS
  • Number of hours: 36h
  • Apogée: GBX9CO03

Basic courses in probability and linear programming.

Pedagogical team#