Universität Wien

390053 DK PhD-L: Advanced Methods in Optimization (2020S)

4.00 ECTS (2.00 SWS), SPL 39 - Doktoratsstudium Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

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

Details

max. 15 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Montag 11.05. 09:45 - 11:15 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
  • Montag 11.05. 11:30 - 13:00 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
  • Montag 11.05. 13:15 - 18:15 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 12.05. 09:45 - 13:00 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 12.05. 13:20 - 18:05 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Mittwoch 13.05. 09:45 - 12:50 Seminarraum 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Mittwoch 13.05. 13:15 - 18:10 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Donnerstag 14.05. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Donnerstag 14.05. 13:15 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Freitag 15.05. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Freitag 15.05. 13:15 - 18:15 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Montag 22.06. 08:00 - 09:30 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Montag 22.06. 09:45 - 14:45 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 23.06. 13:15 - 18:15 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Mittwoch 24.06. 13:00 - 18:15 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Donnerstag 25.06. 08:00 - 18:15 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Freitag 26.06. 08:00 - 18:15 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The problems faced by decision makers in today’s competitive business environment are often
extremely complex and can be addressed by numerous possible courses of action. Evaluating
these alternatives and choosing the best course of action represents the essence of
optimization. This course is devoted to decision science, which considers the application of
mathematical modeling and analysis to managerial problems. A model uses mathematical
relationships to describe or represent a decision problem by means of variables, constraints and
functions. The first part of the course will focus on optimization from a modeling perspective,
and emphasize a structured approach to problem-solving in management situations. The second
part of the course is essentially a tutorial on metaheuristics, providing descriptions,
implementations and practical applications in the area of business analytics.

In the modeling part of the course, the primary objective is to help students become proficient
in developing models and in executing model-based analyses. In other words, students will learn
how to translate business situations into formal models, and to investigate those models in an
organized fashion. To achieve this objective, the course will introduce common analytic
methods, discuss their strength and weaknesses, and show how they can be used to help
managers make better decisions. In particular, we cover two advanced optimization topics:
multi-objective optimization and neural networks. The course will show how to use Excel
spreadsheets to model and solve these problems.

Art der Leistungskontrolle und erlaubte Hilfsmittel

The second part of the course is devoted to metaheuristic approaches. Vitally important
applications not only in business, but also in economics and science cannot be tackled within
practical time horizons by “classical” solution methods in mathematical programming, such as
linear programming or branch and bound. The metaheuristic approaches are dramatically
changing our ability to solve problems of practical significance and are extending the frontier of
problems that can be handled effectively. In this part of the course we will cover some of the
most effective methodologies: GRASP, tabu search, scatter search, and path relinking. To
complement the tutorials on these methods, this course also shows how to design and create a
Visual Basic application in Excel based on metaheuristic principles.

Mindestanforderungen und Beurteilungsmaßstab

Prüfungsstoff

Slides will be made available to the participants.

Literatur

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 13.04.2022 00:30