Decision-Making Under Uncertainty

• Explain techniques for decision-making under uncertainty (stochastic programming, approximate dynamic programming, robust optimization)
• Formulate and solve problems where decisions need to be made sequentially in time.
• Formulate decision-making problems in different applications (energy systems, electricity markets, finance, logistics) as mathematical programs.
• 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
• 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
• Use AI (large language models) productively to support understanding of the course’s concepts.
• 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
• Document, structure and present results in a written report.
• Keep track of one’s own learning process.

Core elements:
* Techniques of optimization under uncertainty: stochastic programming, robust optimization, approximate dynamic programming, sequential decisions, scenario generation

Key concepts: here-and-now vs. recourse decisions; Two-stage and multi-stage stochastic programming; Markov Decision Process; Sequential decisions; value function; Bellman equation; robust and stochastic solutions; worst-case and expected-value optimization; risk aversion; scenario generation; decision rules; value of stochastic solution, expected value of perfect information, etc.

• 6 – 20 (Tues 13-17)

This course uses lectures, exercises, and group assignments.

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.