Universität Wien

040894 KU LP Modeling I (MA) (2024S)

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

Montag 04.03. 11:30 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Montag 11.03. 11:30 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Montag 18.03. 11:30 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Montag 08.04. 13:15 - 14:45 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Dienstag 09.04. 11:30 - 14:45 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Montag 15.04. 11:30 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Montag 22.04. 11:30 - 14:45 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Montag 29.04. 11:25 - 14:45 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

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 and have to be uploaded to Moodle and presented in class. There will be a tutorial (Mar. 18th) for implementing simple LP models in Mosel. On April 9th, students can practice their implementation skills under supervision in the PC lab (attendance is not mandatory).

Art der Leistungskontrolle und erlaubte Hilfsmittel

20 % homework
40 % midterm exam (closed book, on-site) (08.04.2024)
40 % final exam (closed book, on-site) (29.04.2024)

Students have to upload the solutions to their homework in Moodle and present them in class.

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
- Solution methods
- 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 by writing Mosel code on paper (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: Mi 28.02.2024 16:05