Single-Course English 5 ECTS

Decision-Making Under Uncertainty

Overall Course Objectives

To provide the student with the skills to tackle decision-making problems with uncertain information within different application fields (such as energy systems, finance, logistics) by making use of techniques of optimization under uncertainty.

Learning Objectives

  • Explain techniques for decision-making under uncertainty (stochastic programming, robust optimization)
  • Formulate decision-making problems in different applications (energy systems, electricity markets, finance, logistics) as mathematical programs.
  • Apply scenario generation techniques to handle uncertain data as input to the decision-making process.
  • Apply a technique of optimization under uncertainty to a new planning problem
  • Solve optimization problems including uncertainty using appropriate programming languages and software
  • Analyze and interpret the solution to an optimization problem in relation to the planning problem and with regards to quality.
  • Debate the different techniques of optimization under uncertainty in terms of uncertainty modeling, objective function, degree of conservatism and problem structure
  • Explain the concepts of decomposition techniques and heuristic solution methods as techniques to solve large-scale optimization problems
  • Assess and judge the best technique of optimization under uncertainty to be applied to a specific decision-making problem given input information, modeling of uncertainty, risk criterion, sequence of decisions and computational tractability
  • Document, structure and present results in a written report.
  • Keep track of one’s own learning process.

Course Content

Core elements:
* Techniques of optimization under uncertainty: stochastic programming, robust optimization, decomposition techniques, heuristics, scenario generation

Key concepts: here-and-now vs. recourse decisions; 1-stage, 2-stage and multi-stage decision-making processes; robust and stochastic solutions; worst-case and expected-value optimization; risk aversion; heuristics; decomposition techniques; scenario generation; decision rules; value of stochastic solution, expected value of perfect information, etc.

Recommended prerequisites

42112/42002/42111, or similar. This course will build on modeling using mathematical programming. Knowledge of a programming language such as Python or Julia is an advantage, but is not required.

Teaching Method

This course uses lectures, exercises and group assignments.

Limited number of seats

Minimum: 12.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.

See course in the course database.





13 weeks




DTU Lyngby Campus

Course code 02435
Course type Candidate
Semester start Week 5
Semester end Week 19
Days Tues 13-17

7.500,00 DKK

Please note that this course has participants limitation. Read more