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

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

Registration/Deregistration

Details

max. 35 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Thursday 04.03. 09:45 - 11:15 Digital
Thursday 04.03. 11:30 - 13:00 Digital
Thursday 11.03. 09:45 - 11:15 Digital
Thursday 11.03. 11:30 - 13:00 Digital
Thursday 18.03. 09:45 - 11:15 Digital
Thursday 18.03. 11:30 - 13:00 Digital
Thursday 25.03. 09:45 - 11:15 Digital
Thursday 25.03. 11:30 - 13:00 Digital
Wednesday 14.04. 09:45 - 11:15 Digital
Thursday 15.04. 09:45 - 11:15 Digital
Thursday 15.04. 11:30 - 13:00 Digital
Thursday 22.04. 09:45 - 11:15 Digital
Thursday 29.04. 09:45 - 11:15 Digital
Wednesday 05.05. 09:45 - 11:15 Digital

Information

Aims, contents and method of the course

The course introduces students to modeling techniques in the area of linear programming. The underlying aim is to improve the students' problem solving skills. The following topics will be discussed:

Introduction to Linear Programming
Introduction to XPress-MP
Solution methods
Duality
Sensitivity Analysis & its economic interpretation
Introduction to (mixed) integer programming

Students, who take the course, are assumed to have a basic math knowledge (solving equation systems, working with inequalities, matrix multiplication).

Assessment and permitted materials

20 % homework
40 % midterm exam (open book, online) (date April, 14th, 2021)
40 % final exam (open book, online) (date May, 5th, 2021)

Students have to upload the solutions to their homework in Moodle. (updated)

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 21.04.2021 11:25