Universität Wien

040894 KU LP Modeling I (MA) (2019W)

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

  • Friday 04.10. 11:30 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 18.10. 11:30 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 25.10. 11:25 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 08.11. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Wednesday 13.11. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Friday 15.11. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 29.11. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 06.12. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 13.12. 11:30 - 13:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock

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 about the underlying problems and solution techniques the following topics will be discussed:

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

The classes will consist of a lecture part, a discussion of the homework assignments, and programming on the computers in the lab by the students.

Assessment and permitted materials

20 % homework
40 % midterm exam (closed book) (Nov, 13th, 2019)
40 % final exam (closed book) (Dec, 13th, 2019)

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
- 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 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: Mo 07.09.2020 15:20