040897 KU LP Modeling II (MA) (2023S)
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 Mo 13.02.2023 09:00 to We 22.02.2023 12:00
- Registration is open from Mo 27.02.2023 09:00 to Tu 28.02.2023 12:00
- Registration is open from Mo 17.04.2023 14:30 to Th 04.05.2023 23:00
- Deregistration possible until We 10.05.2023 23:00
Details
max. 30 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Thursday
04.05.
09:45 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Thursday
11.05.
09:45 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Thursday
25.05.
09:45 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Thursday
01.06.
09:45 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Thursday
15.06.
09:45 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Thursday
22.06.
09:45 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Friday
07.07.
09:45 - 11:15
Digital
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 Mosel/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.Furthermore, there will be a group homework assignement where students need to understand, possibly adapt and implement an LP model from literature. There will be short presentations of the models during class.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
20 % individual homework assignments
30 % group homework (due: July 2nd, 2023 via Moodle)
10 % presentation (date: June, 15th, 2023)
40 % final exam (closed book; on-site) (date: June 22nd, 2023)
30 % group homework (due: July 2nd, 2023 via Moodle)
10 % presentation (date: June, 15th, 2023)
40 % final exam (closed book; on-site) (date: June 22nd, 2023)
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 understand, formulate and solve a variety of LP models and implement them using Mosel / XpressMP. Slides will be available in Moodle.Part of the exam will consist of the correct formulation and the understanding of models related to
- Transportation / assignment problems
- Transshipment and warehouse location problems
- Vehicle routing problems
- Network flow problems
- Network design problems
- Scheduling
- Lot-sizingFurthermore, the exam will 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.
- Transportation / assignment problems
- Transshipment and warehouse location problems
- Vehicle routing problems
- Network flow problems
- Network design problems
- Scheduling
- Lot-sizingFurthermore, the exam will 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: Tu 27.06.2023 11:47