040897 KU LP Modeling II (MA) (2017S)
Continuous assessment of course work
Labels
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 We 15.02.2017 09:00 to Th 04.05.2017 12:00
- Deregistration possible until Th 11.05.2017 23:59
Details
max. 30 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 04.05. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 11.05. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 18.05. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 01.06. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 08.06. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 22.06. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 29.06. 08:05 - 09:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 29.06. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Information
Aims, contents and method of the course
The course builds upon the knowledge gained in the course LP-Modeling I and introduces students to advanced modeling techniques. In particular, complex linear programming models in the fields of production, logistics and supply chain management are discussed. Besides the modeling aspects, an emphasis is given on the implementation of the models in XpressMP, which is then used to solve these models.In addition to the classes, students are supposed to prepare different homework assignments, which they must be able to explain / present individually. The classes will consist of a short discussion of the homework assignments, a lecture part, and programming on the computers in the lab by the students.At the end of the course students should be able to develop mathematical (linear programming) models for different problems that arise in production and logistics. Moreover, they will have acquired programming skills in Mosel (the programming language of XPress) in order to implement and solve these models by the use of XPressMP.
Assessment and permitted materials
60 % different homework assignements
5 % active participation in class
35 % final exam (closed book)
5 % active participation in class
35 % final exam (closed book)
Minimum requirements and assessment criteria
In order to pass the course (minimum requirement) students have to achieve at least 50% in total.
Examination topics
Students are expected to understand, formulate and solve a variety of LP models and implement them using XpressMP. Slides will be available in Moodle.
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: Mo 07.09.2020 15:29