Modeling decision problems under uncertainty

BEA512 Modeling decision problems under uncertainty

Autumn 2018

  • 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

    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

  • 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.

  • Recommended prerequisites

    It is necessary with solid knowledge of standard optimization theory, especially linear programming. Experience with stochastic programming is not expected. 

  • Required prerequisites

    Basic knowledge of mathematical programming modeling

  • 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

    Pass/Fail

  • 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.

  • Literature

    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.

Overview

ECTS Credits
5
Teaching language
English
Semester

Autumn 2019

Course responsible

Stein W. Wallace, Department of Business and Management Science