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

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 35 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

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

Information

Aims, contents and method of the course

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).

Assessment and permitted materials

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.

Minimum requirements and assessment criteria

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%

Examination topics

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.

Reading list

* 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.

Association in the course directory

Last modified: We 31.07.2024 11:25