040897 PR KFK PM/SCM/TL: Practical Course LP-Modeling II (2016S)
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 17.02.2016 09:00 to We 24.02.2016 12:00
- Deregistration possible until Mo 14.03.2016 23:59
Details
max. 20 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 12.05. 11:30 - 14:45 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 19.05. 11:30 - 14:45 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 02.06. 11:30 - 14:45 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 09.06. 11:30 - 14:45 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 16.06. 11:30 - 14:45 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 23.06. 11:30 - 14:45 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 23.06. 15:00 - 16:30 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 30.06. 11:30 - 14:45 PC-Seminarraum 1 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.The classes will consist of a revision of the homework assignments, a lecture part, and programming on the computers in the lab by the students. In addition to the classes, students are supposed to prepare homework assignments and a term project.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
The grading is based on the following tasks.* [25%] homework assignments
* [40%] project: implement a complex model and write a short paper
* [35%] exam: write models (closed book)With regard to the homework assignments, the following things have to be taken into account: The exercises for the homework will be available on the e-learning platform Moodle, where students also have to upload their files with the code for XPress. There will also be a list on moodle ("Kreuzerlliste") where the students have to announce the assignments that they have prepared. The files have to be uploaded 2 hours before class (at the latest). The same applies for setting the ticks on the list. At the beginning of the class the assignments will be discussed. Students will be picked randomly in order to present their homework. In case one fails to explain what was asked in the exercise, one gets the homework of the respective session canceled.
* [40%] project: implement a complex model and write a short paper
* [35%] exam: write models (closed book)With regard to the homework assignments, the following things have to be taken into account: The exercises for the homework will be available on the e-learning platform Moodle, where students also have to upload their files with the code for XPress. There will also be a list on moodle ("Kreuzerlliste") where the students have to announce the assignments that they have prepared. The files have to be uploaded 2 hours before class (at the latest). The same applies for setting the ticks on the list. At the beginning of the class the assignments will be discussed. Students will be picked randomly in order to present their homework. In case one fails to explain what was asked in the exercise, one gets the homework of the respective session canceled.
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 write mathematical models and implement them using XpressMP.
Reading list
Introduction to Linear Optimization, by John Tsitsiklis and Dimitris Bertsimas
Association in the course directory
Last modified: Mo 07.09.2020 15:29