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2022S, 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.

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

LVA Termine

TagZeitDatumOrtBeschreibung
13:00 - 16:0001.03.2022 - 11.03.2022Seminarraum FAV 05 (Seminarraum 186) 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Di.13:00 - 16:0008.03.2022Seminarraum FAV 05 (Seminarraum 186) 186.861: Modeling and Solving Constrained Optimization Problems
Metaheuristics and Hybrid Methods for Combinatorial Optimization - Einzeltermine
TagDatumZeitOrtBeschreibung
Di.01.03.202213:00 - 16:00Seminarraum FAV 05 (Seminarraum 186) 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Mi.02.03.202213:00 - 16:00Seminarraum FAV 05 (Seminarraum 186) 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Do.03.03.202213:00 - 16:00Seminarraum FAV 05 (Seminarraum 186) 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Fr.04.03.202213:00 - 16:00Seminarraum FAV 05 (Seminarraum 186) 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Mo.07.03.202213:00 - 16:00Seminarraum FAV 05 (Seminarraum 186) 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Di.08.03.202213:00 - 16:00Seminarraum FAV 05 (Seminarraum 186) 186.861: Modeling and Solving Constrained Optimization Problems
Mi.09.03.202213:00 - 16:00Seminarraum FAV 05 (Seminarraum 186) 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Do.10.03.202213:00 - 16:00Seminarraum FAV 05 (Seminarraum 186) 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Fr.11.03.202213:00 - 16:00Seminarraum FAV 05 (Seminarraum 186) 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
07.02.2022 03:30 03.03.2022 03:30 03.03.2022 03:30

Anmeldemodalitäten

Please register for this course via TISS.

Curricula

StudienkennzahlVerbindlichkeitSemesterAnm.Bed.Info
066 645 Data Science Freifach
175 FW Freie Wahlfächer - Wirtschaftsinformatik Freifach
880 FW Freie Wahlfächer - Informatik Freifach

Literatur

Es wird kein Skriptum zur Lehrveranstaltung angeboten.

Vorkenntnisse

Solid knowledge in programming, algorithms and data structures.

Englisch