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040894 KU LP Modeling I (MA) (2023S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 13.02.2023 09:00 bis Mi 22.02.2023 12:00
- Anmeldung von Mo 27.02.2023 09:00 bis Di 28.02.2023 12:00
- Abmeldung bis Fr 17.03.2023 23:59
Details
max. 35 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
The course this semester will take place on-site at OPM. The class content will be provided on a weekly basis. Correspondingly, there will be homework examples every week that have to be solved individually.
- Donnerstag 02.03. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 09.03. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 16.03. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 22.03. 09:45 - 13:00 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 23.03. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 29.03. 13:15 - 14:45 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Donnerstag 20.04. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 27.04. 09:45 - 13:00 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
20 % homework
40 % midterm exam (closed book, on-site) (date March, 29th, 2023)
40 % final exam (closed book, on-site) (date April, 27th, 2023)Students have to upload the solutions of their homework in Moodle and present them in class.
40 % midterm exam (closed book, on-site) (date March, 29th, 2023)
40 % final exam (closed book, on-site) (date April, 27th, 2023)Students have to upload the solutions of their homework in Moodle and present them in class.
Mindestanforderungen und Beurteilungsmaßstab
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%
Prüfungsstoff
Students are expected to be able to understand, formulate and solve a variety of LP models in the exam and implement them using Mosel / XpressMP. Slides will be available in Moodle.Content of the exams:
- Formulation of LP models
- Solution methods
- Duality
- Sensitivity analysis
- Mosel / XPress
- Branch-and-bound
- Modeling with binary variables
- Formulation of specific objectivesThe final exam will additionally 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.
- Formulation of LP models
- Solution methods
- Duality
- Sensitivity analysis
- Mosel / XPress
- Branch-and-bound
- Modeling with binary variables
- Formulation of specific objectivesThe final exam will additionally 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.
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: Di 14.03.2023 11:28
Introduction to Mosel / XPress-MP
Simplex Method (brief repetition)
Sensitivity Analysis & its economic interpretation
Introduction to (mixed) integer programming
Modeling with binary variablesNew content will be provided weekly in class. Homework examples have to be solved individually and have to be uploaded to Moodle and presented in class. There will be a tutorial (Mar. 16th) for implementing simple LP models in Mosel. On March 22nd, students can practice their implementation skills under supervision in the PC lab (attendance not mandatory).