Universität Wien

040894 KU LP Modeling I (MA) (2021S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
DIGITAL
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 04.03. 09:45 - 11:15 Digital
  • Donnerstag 04.03. 11:30 - 13:00 Digital
  • Donnerstag 11.03. 09:45 - 11:15 Digital
  • Donnerstag 11.03. 11:30 - 13:00 Digital
  • Donnerstag 18.03. 09:45 - 11:15 Digital
  • Donnerstag 18.03. 11:30 - 13:00 Digital
  • Donnerstag 25.03. 09:45 - 11:15 Digital
  • Donnerstag 25.03. 11:30 - 13:00 Digital
  • Mittwoch 14.04. 09:45 - 11:15 Digital
  • Donnerstag 15.04. 09:45 - 11:15 Digital
  • Donnerstag 15.04. 11:30 - 13:00 Digital
  • Donnerstag 22.04. 09:45 - 11:15 Digital
  • Donnerstag 29.04. 09:45 - 11:15 Digital
  • Mittwoch 05.05. 09:45 - 11:15 Digital

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The course introduces students to modeling techniques in the area of linear programming. The underlying aim is to improve the students' problem solving skills. The following topics will be discussed:

Introduction to Linear Programming
Introduction to XPress-MP
Solution methods
Duality
Sensitivity Analysis & its economic interpretation
Introduction to (mixed) integer programming

Students, who take the course, are assumed to have a basic math knowledge (solving equation systems, working with inequalities, matrix multiplication).

Art der Leistungskontrolle und erlaubte Hilfsmittel

20 % homework
40 % midterm exam (open book, online) (date April, 14th, 2021)
40 % final exam (open book, online) (date May, 5th, 2021)

Students have to upload the solutions to their homework in Moodle. (updated)

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: Fr 12.05.2023 00:13