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040894 KU LP Modeling I (MA) (2021S)
Continuous assessment of course work
Labels
REMOTE
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- 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
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
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)
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%
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 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.
- Formulation of LP models
- Solution methods
- Duality
- Sensitivity analysis
- Mosel / XPress
- Branch-and-bound
- 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.
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.
* 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: Fr 12.05.2023 00:13
Introduction to XPress-MP
Solution methods
Duality
Sensitivity Analysis & its economic interpretation
Introduction to (mixed) integer programmingStudents, who take the course, are assumed to have a basic math knowledge (solving equation systems, working with inequalities, matrix multiplication).