040974 UK Methods of Decision Support (2019S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 11.02.2019 09:00 to We 20.02.2019 12:00
- Deregistration possible until Th 14.03.2019 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 07.03. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Thursday 14.03. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Thursday 21.03. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Thursday 28.03. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Thursday 04.04. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Thursday 11.04. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Thursday 02.05. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Thursday 09.05. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Thursday 16.05. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Thursday 23.05. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Thursday 06.06. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Thursday 13.06. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
- Friday 28.06. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
Information
Aims, contents and method of the course
The aim of the course is to get acquainted with the basic concepts in the areas combinatorial optimization, dynamic optimization, and multicriteria decision analysis. Mathematical representations of important problem types (usually based on linear programming formulations) are introduced, and solution techniques are outlined. We shall discuss exact as well as heuristic solution methods. Among others, we will consider branch-and-bound methods, greedy algorithms, local search, simulated annealing, variable neighborhood search, genetic algorithms, dynamic programming, and epsilon-constraint methods.
Assessment and permitted materials
There is no compulsory attendance at the lectures. At the beginning of (most of) the lectures students can volunteer to present their solutions to exercises prepared at home. At the end of the term there will be an (optional) written exam in case the exercise presentations have not been sufficient or satisfactory.
Minimum requirements and assessment criteria
Each student has to give at least three exercise presentations (in at least two different topics) in a satisfactory quality. If at the end of the course this goal is not reached, there is the optional possibility of attending a written exam.
Examination topics
All topics covered in the lecture.
Reading list
Necessary material (including a collection of exercises) will be made available to the participants.
Association in the course directory
Last modified: Mo 07.09.2020 15:29