186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Diese Lehrveranstaltung ist in allen zugeordneten Curricula Teil der STEOP.
Diese Lehrveranstaltung ist in mindestens einem zugeordneten Curriculum Teil der STEOP.

2021W, VU, 2.5h, 4.0EC, wird geblockt abgehalten

Merkmale

  • Semesterwochenstunden: 2.5
  • ECTS: 4.0
  • Typ: VU Vorlesung mit Übung
  • Format der Abhaltung: Präsenz

Lernergebnisse

Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage verschiedene heuristische und hybride Algorithmen zur Lösung schwieriger kombinatorischer Optimierungsprobleme zu verstehen, anzuwenden und für neue Probleme zu adaptieren.

Inhalt der Lehrveranstaltung

Combinatorial optimization problems arise in many aspects of human activities. Examples concern packing and cutting problems, timetabling problems, and vehicle routing problems. Many of these problems are computationally very difficult to be solved to optimality. Therefore, simple heuristics (such as greedy algorithms) and metaheuristics (such as tabu search, evolutionary algorithms, and simulated annealing) have achieved a lot of attention from the optimization community during the last decades. At the same time, the operations research community has invested considerable efforts into both exact techniques (such as algorithms based on branch & bound) and general purpose solvers that implement these state-of-the-art exact techniques. Example are CPLEX and Gurobi. This course will give an introduction to these topics. Attendees should bring a laptop with a recent Linux operating system (e.g. Ubuntu) and the GNU g++ compiler installed.

Methoden

Introduction and explanation of methods, discussion of examples, theoretical exercises, hands-on programming exercises, presentation and discussion of solutions.

Prüfungsmodus

Mündlich

Weitere Informationen

ECTS Breakdown:

* 20 hours of theory classes (0.8 ECTS)
* 10 hours of reading homework (0.4 ECTS)
* 70 hours work on the practical project: 2.8 ECTS. Approximate division of these 70 hours:
  + Getting familiar with the given problem and the required tasks: 5 hours
  + Design and implementation of the heuristics and metaheuristics: 20 hours
  + Design and implementation of the hybrid metaheuristics: 10 hours
  + Experimental evaluation: 15 hours
  + Writing the report: 20 hours

Vortragende Personen

Institut

LVA Termine

TagZeitDatumOrtBeschreibung
Mo.11:00 - 13:0017.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Di.12:00 - 14:0018.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Mi.12:00 - 15:0019.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Do.10:00 - 13:0020.01.2022EI 3A Hörsaal 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Fr.12:00 - 14:0021.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Mo.11:00 - 13:0024.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Di.12:00 - 14:0025.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Mi.12:00 - 15:0026.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Do.10:00 - 13:0027.01.2022EI 3A Hörsaal 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Fr.12:00 - 15:0028.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
LVA wird geblockt abgehalten

Leistungsnachweis

Programming assignments, discussion of results, and oral exam.

LVA-Anmeldung

Von Bis Abmeldung bis
22.09.2021 00:00 20.01.2022 23:59

Curricula

Literatur

Es wird kein Skriptum zur Lehrveranstaltung angeboten.

Vorkenntnisse

Solid knowledge in programming, algorithms and data structures.

Sprache

Englisch