040894 KU LP Modeling I (MA) (2020S)
- Registration is open from Mo 10.02.2020 09:00 to We 19.02.2020 12:00
- Registration is open from Tu 25.02.2020 09:00 to We 26.02.2020 12:00
- Deregistration possible until Th 30.04.2020 23:59
Classes (iCal) - next class is marked with N
The midterm exam is shifted to May 6th, and the final exam to May 20th. The exams will take place online via Moodle.Please see Moodle for more detailed information on the changes to the course due to the corona virus.
Aims, contents and method of the course
Assessment and permitted materials
40 % midterm exam (closed book) (date May, 6th, 2020) (updated)
40 % final exam (closed book) (date May, 20th, 2020)Students have to upload the solutions to their homework in Moodle. (updated)
Minimum requirements and assessment criteria
4: 50% to <63%
3: 63% to <75%
2: 75% to <87%
1: 87% to 100%
- Formulation of LP models
- Solution methods
- Sensitivity analysis
- Mosel / XPress
- Modeling with binary variables
- Formulation of specific objectivesThe 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.
* 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.