040897 KU LP Modeling II (MA) (2017S)
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 15.02.2017 09:00 bis Do 04.05.2017 12:00
- Abmeldung bis Do 11.05.2017 23:59
Details
max. 30 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Donnerstag 04.05. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 11.05. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 18.05. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 01.06. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 08.06. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 22.06. 09:45 - 12:55 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 29.06. 08:05 - 09:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 29.06. 09:45 - 12:55 PC-Seminarraum 5 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.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.
Art der Leistungskontrolle und erlaubte Hilfsmittel
60 % different homework assignements
5 % active participation in class
35 % final exam (closed book)
5 % active participation in class
35 % final exam (closed book)
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 understand, formulate and solve a variety of LP models and implement them using XpressMP. Slides will be available in Moodle.
Literatur
* 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.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 07.09.2020 15:29