040894 KU LP Modeling I (MA) (2021S)
- Registration is open from Th 11.02.2021 09:00 to Mo 22.02.2021 12:00
- Registration is open from Th 25.02.2021 09:00 to Fr 26.02.2021 12:00
- Deregistration possible until We 31.03.2021 23:59
Classes (iCal) - next class is marked with N
Aims, contents and method of the course
Assessment and permitted materials
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
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.