Syllabus

LEARNING OUTCOMES

Upon completing this course, the student should

• understand how optimisation models can be enhanced to consider uncertainty in the input data;

• understand the main techniques for modelling and solving optimisation problems under uncertainty

  in practice;

• know how to use optimisation software for implementing and solving stochastic programming and

  robust optimisation problems.

Credits: 5

Schedule: 03.09.2024 - 28.11.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Fabricio Pinheiro de Oliveira

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

• valid for whole curriculum period:

  1. Two- and multi-stage stochastic programming
  2. Scenario generation and sampling average approximation
  3. Chance constraints and risk management
  4. Static and adjustable robust optimisation
  5. Specialised solution methods

Assessment Methods and Criteria

• valid for whole curriculum period:

  • attendance
  • seminar presentation;
  • project work.

Workload

• valid for whole curriculum period:

  • lectures;
  • tutorial sessions;
  • guided self-study;
  • seminar presentations;
  • project work and feedback.

DETAILS

Study Material

• valid for whole curriculum period:

  • lecture notes and slides
  • tutorial examples
  • scientific papers

Substitutes for Courses

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  Prerequisites

FURTHER INFORMATION

Further Information

• valid for whole curriculum period:

  Teaching Language: English

  Teaching Period: 2024-2025 Autumn I - II
  2025-2026 Autumn I - II

Description