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

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften

Continuous assessment of course work

MIXED

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 13.09.2021 09:00 to Th 23.09.2021 12:00
Registration is open from Mo 27.09.2021 09:00 to We 29.09.2021 12:00
Deregistration possible until Fr 15.10.2021 23:59

Details

max. 35 participants

Language: English

Lecturers

Classes (iCal) - next class is marked with N

The course format this semester will be hybrid. New content will be mostly discussed in live online classes. On Nov. 26th, there will be a the possibility to practice programming on-site at OMP.
Exams will take place on-site at OMP on Nov. 18th, and Dec. 16th, closed book.
The mode might be adaped if Corona rules change.

    
    Thursday
    07.10.
    09:45 - 13:00
    Digital
    
    Thursday
    14.10.
    09:45 - 13:00
    Digital
    
    Thursday
    21.10.
    09:45 - 13:00
    Digital
    
    Thursday
    28.10.
    09:45 - 13:00
    Digital
    
    Thursday
    04.11.
    09:45 - 13:00
    Digital
    
    Thursday
    11.11.
    09:45 - 13:00
    Digital
    
    Thursday
    18.11.
    09:45 - 13:00
    PC-Seminarraum 2  Oskar-Morgenstern-Platz 1  1.Untergeschoß
PC-Seminarraum 3  Oskar-Morgenstern-Platz 1  1.Untergeschoß
PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Friday
    19.11.
    09:45 - 13:00
    Digital
    
    Thursday
    25.11.
    09:45 - 13:00
    Digital
    
    Friday
    26.11.
    09:45 - 13:00
    PC-Seminarraum 3  Oskar-Morgenstern-Platz 1  1.Untergeschoß
PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Thursday
    02.12.
    09:45 - 11:10
    Digital
    
    Thursday
    16.12.
    09:45 - 13:00
    Hörsaal 5  Oskar-Morgenstern-Platz 1  Erdgeschoß
Hörsaal 8  Oskar-Morgenstern-Platz 1  1.Stock
    
    Thursday
    13.01.
    09:45 - 11:15
    PC-Seminarraum 1  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 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

New content will be provided as slides with audio comments. Homework examples have to be solved individually and have to be uploaded in Moodle. There will be an online tutorial (Nov. 19th) for implementing simple LP models in Mosel. On Nov 26th students can practice their implementation skills under supervision in the PC lab (attendance not mandatory).

Assessment and permitted materials

20 % homework
40 % midterm exam (on-site) (Nov, 18th, 2021)
40 % final exam (onsite) (Dec, 16th, 2021)

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 (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: Fr 12.05.2023 00:13