040894 KU LP Modeling I (MA) (2019W)
- Registration is open from Mo 16.09.2019 09:00 to Mo 23.09.2019 12:00
- Registration is open from Th 26.09.2019 09:00 to Fr 27.09.2019 12:00
- Deregistration possible until Mo 14.10.2019 12:00
Classes (iCal) - next class is marked with N
Aims, contents and method of the course
Assessment and permitted materials
40 % midterm exam (closed book) (Nov, 13th, 2019)
40 % final exam (closed book) (Dec, 13th, 2019)
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
- Graphical solution method
- The Simplex algorithm
- 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.