Universität Wien

040894 KU LP Modeling I (MA) (2023W)

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

An/Abmeldung

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

Details

max. 35 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Donnerstag 05.10. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Donnerstag 12.10. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Donnerstag 19.10. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Donnerstag 09.11. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Freitag 10.11. 09:45 - 13:00 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Donnerstag 16.11. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Donnerstag 23.11. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Donnerstag 30.11. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Donnerstag 07.12. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Donnerstag 07.12. 11:30 - 13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The course introduces students to modeling techniques in the area of linear programming. To gain a better understanding of the underlying problems and solution techniques, we will discuss the following topics:

Introduction to Linear Programming
Introduction to Mosel / XPress-MP
Simplex Method (brief repetition)
Sensitivity Analysis & its economic interpretation
Introduction to (mixed) integer programming
Modeling with binary variables

New content will be provided weekly in class. Homework examples have to be solved individually. An introductory lesson (Mar. 18th) will be held for implementing simple LP models in Mosel. There will be an additional tutorial, where students can practice their implementation skills under supervision in the PC lab (attendance not mandatory).

Art der Leistungskontrolle und erlaubte Hilfsmittel

20 % homework
40 % midterm exam (closed book, on-site) (date April, 8th, 2024)
40 % final exam (closed book, on-site) (date April, 29th, 2024)

Mindestanforderungen und Beurteilungsmaßstab

In order to pass the course (minimum requirement) students have to achieve at least 50% in total.

The other grades are distributed as follows:
4: 50% to <63%
3: 63% to <75%
2: 75% to <87%
1: 87% to 100%

Prüfungsstoff

Students are expected to be able to understand, formulate and solve a variety of LP models in the exam and implement them using Mosel / XpressMP. Slides will be available in Moodle.

Content of the exams:
- Formulation of LP models
- Graphical solution method
- The Simplex algorithm
- Duality
- Sensitivity analysis
- Mosel / XPress
- Branch-and-bound
- Modeling with binary variables
- Formulation of specific objectives

The final exam will additionally include parts where students need to show the implementation skills acquired during lessons and homework (e.g. how the implementation of a certain constraint would look like, how one has to declare variables, etc.) and by explaining a given Mosel code and/or finding errors in it.

Literatur

* Bertsimas, D., & Tsitsiklis, J. N. (1997). Introduction to linear optimization. Athena Scientific.
* Papadimitriou, C. H., & Steiglitz, K. (1998). Combinatorial Optimization: Algorithms and Complexity. Dover Publications.
* Guéret, C., Prins, C., & Sevaux, M. (2002). Applications of optimisation with Xpress-MP. Dash optimization.
* Hillier, F. S., & Lieberman, G. J. Introduction to Operations Research. McGraw-Hill.
* Anderson, D. R., Sweeney, D. J. An introduction to management science: quantitative approaches to decision making. South-Western.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 12.02.2024 12:05