BEA512 Modeling Decision Problems under Uncertainty
The course will give the students in-depth knowledge of how to include uncertainty into mathematical planning models, with a focus on mathematical programming. There will be an emphasis on how to model uncertainty to obtain meaningful models for planning purposes within logistics / supply chains as well as resource planning. Basic algorithmic structures will also be presented, but the course is primarily modeling oriented, not algorithmically oriented.
The course will cover:
- The role of what-if analysis and parametric optimization in decision-making under uncertainty
- How to handle feasibility in optimization under uncertainty
- Basic algorithmic structures for stochastic programming
- Scenario generation, that is, how to represent uncertainty in mathematical programming models
- Alternative modeling concepts for handling uncertainty
- Stochastic network design
- Stochastic facility layout
- Examples from energy systems modeling.
Knowledge: Upon completion the student
- understands how uncertainty can be included in optimization models
- knows about the main algorithmic approaches to stochastic programming
- knows how uncertain phenomena can be modeled to fit into stochastic programs
Skills: Upon completion the student is
- able to argue coherently about uncertainty in mathematical programming models
- able to see whether or not appropriate tools have been used by others when setting up models
Competence: Upon completion the student will
- Be able to formulate, understand and solve stochastic programming problems and make sure correct tools are used.
Lectures and student presentations. The course will meet three times during the term, each time lunch to lunch, covering two full days, typically lunch Wednesday to lunch Friday.
It is necessary with solid knowledge of standard optimization theory, especially linear programming. Experience with stochastic programming is not expected.
Basic knowledge of mathematical programming modeling
Requirements for course approval
Presence is required.
Each student must hand in a term paper after the course is finished, where the theories of the course are used on a mathematical programming model, preferably one the student is already working with in his / her thesis. Deadline will be end of January.
It is an advantage to know advanced modeling languages, such as AMPL, MPL or GAMS, but not required. But this will not be taught. A good basis would be ENE420 or something equivalent.
Alan King and Stein W. Wallace, Modeling with stochastic programming, Springer, 2012.
Peter Kall and Stein W. Wallace, Stochastic Programming, Wiley, Chichester, 1994. Available for free.
Articles published in scientific journals.
- ECTS Credits
- Teaching language
Autumn. Offered Autumn 2020.
Please note: Due to the present corona situation, please expect parts of this course description to be changed before the autumn semester starts. Particularly, but not exclusively, this relates to teaching methods, mandatory requirements and assessment.
Stein W. Wallace, Department of Business and Management Science