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040894 KU LP Modeling I (MA) (2020W)

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

Registration/Deregistration

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

Details

max. 35 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Most of the content of the class will be provided on a weekly basis online in form of slides with audio comments. Correspondingly, there will be homework examples every week which have to be solved individually and have to be uploaded in Moodle.

Live online sessions will be held on Oct. 1st, where the course modalities will be discussed, Nov. 5th (Mosel tutorial) and Oct. 22nd and Dec. 3rd (Q&A sessions for the exams).

Instead of the classes planned for November in the PC Lab, which have to be cancelled due to the Corona situation, we will have a live session on Nov. 11th, 16:45.

The midterm exam will be held on Oct. 29th, the final exam on Dec. 10th, both online.

Additional details and updates will be provided in Moodle.

Thursday 01.10. 11:30 - 13:00 Digital (Kickoff Class)
Thursday 22.10. 11:30 - 13:00 Digital
Thursday 29.10. 11:30 - 13:00 Digital
Thursday 05.11. 11:30 - 13:00 Digital
Wednesday 11.11. 16:45 - 20:00 Digital
Thursday 03.12. 11:30 - 13:00 Digital
Thursday 10.12. 11:30 - 13:00 Digital

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 tutorials for implementing simple LP models in Mosel, on the one hand online, where an live illustration of an implementation will take place and on the other hand in the PC Lab where students can practice their implementation skills under supervision.

Assessment and permitted materials

20 % homework
40 % midterm exam (online) (Oct, 29th, 2020)
40 % final exam (online) (Dec, 10th, 2020)

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: Mo 16.11.2020 16:27