Uncertainty in Optimization

BAN437 Uncertainty in Optimization

Autumn 2022

Spring 2023
  • Topics

    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.

  • Learning outcome

    By the end of this seminar the students

    Knowledge

    • 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.

    Skills

    • are able to formulate simple optimization models involving uncertainty
    • have developed good skills to write codes for models involving uncertainty.

    General competences

    • are able to use computational tools for implementing and solving a decision model involving uncertainty

  • Teaching

    There will be one week with a mix of teaching and work on the implementation of some simple models using AMPL.

  • Required prerequisites

    Knowledge of BAN402 or equivalent

  • Credit reduction due to overlap

    None.

  • Compulsory Activity

    Must be present and take part in computer related work after lunch on Day 2 and all day on Day 4.

  • Assessment

    Individual essay within two weeks of the end of the seminar

  • Grading Scale

    Pass-Fail

  • Computer tools

    Standard laptop, AMPL modelling language with solvers CPLEX, Gurobi and MINOS (licenses will be provided during the course).

  • Literature

    To be given in Canvas

Overview

ECTS Credits
2.5
Teaching language
English
Semester

Spring. Offered Spring 2022 (first time).

Course responsible

Professor Stein W. Wallace, Department of Business and Management Science