040897 PR KFK PM/SCM/TL: Praktikum LP-Modeling II (2016S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mi 17.02.2016 09:00 bis Mi 24.02.2016 12:00
- Abmeldung bis Mo 14.03.2016 23:59
Details
max. 20 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Donnerstag
12.05.
11:30 - 14:45
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Donnerstag
19.05.
11:30 - 14:45
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Donnerstag
02.06.
11:30 - 14:45
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Donnerstag
09.06.
11:30 - 14:45
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Donnerstag
16.06.
11:30 - 14:45
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Donnerstag
23.06.
11:30 - 14:45
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Donnerstag
23.06.
15:00 - 16:30
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Donnerstag
30.06.
11:30 - 14:45
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
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.
Art der Leistungskontrolle und erlaubte Hilfsmittel
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.
Mindestanforderungen und Beurteilungsmaßstab
In order to pass the course (minimum requirement) students have to achieve at least 50% in total.
Prüfungsstoff
Students are expected to write mathematical models and implement them using XpressMP.
Literatur
Introduction to Linear Optimization, by John Tsitsiklis and Dimitris Bertsimas
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 07.09.2020 15:29