Modeling decision problems under uncertainty

BEA512 Modeling decision problems under uncertainty

  • Topics


    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.

  • Learning outcome

    Learning outcome

    After completing the course, students will be able to:



    handle uncertainty in the context of mathematical planning models, particularly mathematical programming

    model complex, time-dependent planning problems facing uncertainty in such as demand and prices.

    critically analyze published material on uncertainty in planning

    generate scenarios for stochastic optimization, to end up with numerically efficient models.

    review, assess and utilize relevant scientific papers addressing stochastic programming

    General competence:

  • Teaching


    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.

  • Required prerequisites

    Required prerequisites

    Basic knowledge of mathematical programming modeling

  • Requirements for course approval

    Requirements for course approval

    Presence is required.

  • Assessment


    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.

  • Grading Scale

    Grading Scale


  • Computer tools

    Computer tools

    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.

  • Semester


    Autumn 2017

  • Literature


    Alan King and Stein W. Wallace, Modeling with stochastic programming, Springer, 2012.

    Peter Kall and Stein W. Wallace, Stochastic Programming, Wiley, Chichester, 1994.

    Articles published in scientific journals.


ECTS Credits
Teaching language

Course responsible

Stein W. Wallace, Department of Business and Management Science