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

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).

Registration is open from Mo 11.02.2019 09:00 to We 20.02.2019 12:00
Registration is open from Tu 26.02.2019 09:00 to We 27.02.2019 12:00
Deregistration possible until Th 14.03.2019 23:59

Details

max. 35 participants

Language: English

Lecturers

Andrea Seidl

Classes (iCal) - next class is marked with N

    
    Thursday
    07.03.
    09:45 - 13:00
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Thursday
    14.03.
    09:45 - 13:00
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Thursday
    21.03.
    09:45 - 13:00
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Wednesday
    27.03.
    13:15 - 14:45
    Hörsaal 4  Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Thursday
    28.03.
    09:45 - 13:00
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Thursday
    04.04.
    09:45 - 13:00
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Thursday
    11.04.
    09:45 - 13:00
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Thursday
    02.05.
    11:30 - 13:00
    Hörsaal 4  Oskar-Morgenstern-Platz 1  Erdgeschoß

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 XPress-MP
Simplex Method (brief repetition)
Sensitivity Analysis & its economic interpretation
Introduction to (mixed) integer programming

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

25 % homework
35 % midterm exam (closed book) (date March, 27th, 2019)
40 % final exam (closed book) (date May, 2nd, 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 15:29