BAN437 Uncertainty in Optimization
The seminar will introduce uncertainty, in the form of random variables, into optimization models. The focus will be on why we need this, and what can go wrong with deterministic modeling. We shall use professional software to solve numerical models, so as to see how solutions change to adjust to the uncertain future. In other words, see how tactical and strategic decisions change to facilitate operational handling of uncertainty.
By the end of this seminar the students
- are able to explain and discuss key concepts in decision making under uncertainty
- are able to understand works published in major scientific journals and formulate relevant research questions where uncertainty in optimization is involved.
- are able to formulate simple optimization models involving uncertainty
- have developed good skills to write codes for models involving uncertainty.
- are able to use computational tools for implementing and solving a decision model involving uncertainty
There will be one week with a mix of teaching and work on the implementation of some simple models using AMPL.
Knowledge of basic decision modeling using mathematical programming, including the use of high-level software (like AMPL or GAMS - not just Excel) needed. For NHH students, BAN402 is a perfect background.
Credit reduction due to overlap
Must be present and take part in computer related work after lunch on Day 2 and all day on Day 4.
Individual essay within two weeks of the end of the seminar.
Standard laptop, AMPL modelling language with solvers CPLEX, Gurobi and MINOS (licenses will be provided during the course).
To be given in Canvas
- ECTS Credits
- Teaching language
Spring. Offered Spring 2023 (first week of the semester).
Part of studies
Professor Stein W. Wallace, Department of Business and Management Science