Universität Wien FIND

Kehren Sie für das Sommersemester 2022 nach Wien zurück. Wir planen Lehre überwiegend vor Ort, um den persönlichen Austausch zu fördern. Digitale und gemischte Lehrveranstaltungen haben wir für Sie in u:find gekennzeichnet.

Es kann COVID-19-bedingt kurzfristig zu Änderungen kommen (z.B. einzelne Termine digital). Informieren Sie sich laufend in u:find und checken Sie regelmäßig Ihre E-Mails.

Lesen Sie bitte die Informationen auf https://studieren.univie.ac.at/info.

040676 KU Metaheuristics (MA) (2020W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung


Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").


max. 30 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert

Mittwoch 07.10. 11:30 - 13:00 Digital
Mittwoch 14.10. 11:30 - 13:00 Digital
Mittwoch 21.10. 11:30 - 13:00 Digital
Mittwoch 28.10. 11:30 - 13:00 Digital
Mittwoch 04.11. 11:30 - 13:00 Digital
Mittwoch 11.11. 11:30 - 13:00 Digital
Mittwoch 18.11. 11:30 - 13:00 Digital
Mittwoch 25.11. 11:30 - 13:00 Digital
Mittwoch 02.12. 11:30 - 13:00 Digital
Mittwoch 09.12. 11:30 - 13:00 Digital
Mittwoch 16.12. 11:30 - 13:00 Digital
Mittwoch 13.01. 11:30 - 13:00 Digital
Mittwoch 20.01. 11:30 - 13:00 Digital
Mittwoch 27.01. 11:30 - 13:00 Digital


Ziele, Inhalte und Methode der Lehrveranstaltung

Metaheuristics are general high-level procedures that coordinate simple heuristics and rules to find high-quality solutions to difficult optimization problems. They are based on distinct paradigms and offer different mechanisms to go beyond the first solution obtained that cannot be improved by local search. They are frequently built upon a number of common building blocks such as greedy algorithms, randomization, neighborhoods and local search, reduced neighborhoods and candidate lists, intensification, diversification, path-relinking, and periodical restarts. Metaheuristics are among the most effective solution strategies for solving combinatorial optimization problems in practice and very frequently produce much better solutions than those obtained by the simple heuristics and rules they coordinate.
Metaheuristics are particularly attractive in the efficient and effective solution of logistic decision problems in supply chains, transportation, telecommunications, vehicle routing and scheduling, manufacturing and machine scheduling, timetabling, sports scheduling, facility location and layout, and network design, among other areas.

The objective of this course is to provide students with the fundamental tools for designing, tuning, and testing heuristics and metaheuristics for hard combinatorial optimization problems. Besides that, we will also cover the fundamental concepts of complexity theory that are the key to understanding the need for approximate approaches and to design efficient heuristics and metaheuristics.
1. A gentle introduction to the analysis of algorithms and complexity theory
2. Historical and modern local search methods
3. Nature-inspired metaheuristics
4. Construction-based metaheuristics

Art der Leistungskontrolle und erlaubte Hilfsmittel

* [40%] Four short exams á ca. 15 minutes (written, 10% each)
* [45%] Project work (choose one):
- Programming a metaheuristic for an optimisation problem
- Read and study (i.e., summarise, analyse and criticise) a scientific paper
* [15%] Oral presentation of project

Mindestanforderungen und Beurteilungsmaßstab

In order to obtain a positive grade on the course, at least 50% of the overall points have to be achieved. The grades are distributed as follows:
1: 87% to 100%
2: 75% to <87%
3: 63% to <75%
4: 50% to <63%
5: <50%


* Analysis of algorithms and complexity theory (basics)
* Local search methods
* Nature-inspired metaheuristics
* Construction-based metaheuristics


The teaching material (slides, sample code, further reading, etc.) is available on the e-learning platform Moodle.

Useful literature:
1. M. Gendreau and J.-Y. Potvin (2010), editors, Handbook of Metaheuristics, 2nd edition, Springer, 648 pages.
2. E. K. Burke and G. Kendall (2014), editors, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, 2nd edition, Springer, 716 pages.
3. H. H. Hoos and T. Stützle (2005), Stochastic Local Search: Foundations and Applications, Elsevier, 658 pages.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 05.10.2020 10:08