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040894 KU LP Modeling I (MA) (2020W)
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 14.09.2020 09:00 bis Mi 23.09.2020 12:00
- Abmeldung bis Di 27.10.2020 12:00
Details
max. 35 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Most of the content of the class will be provided on a weekly basis online in form of slides with audio comments. Correspondingly, there will be homework examples every week which have to be solved individually and have to be uploaded in Moodle.
Live online sessions will be held on Oct. 1st, where the course modalities will be discussed, Nov. 5th (Mosel tutorial) and Oct. 22nd and Dec. 3rd (Q&A sessions for the exams).Instead of the classes planned for November in the PC Lab, which have to be cancelled due to the Corona situation, we will have a live session on Nov. 11th, 16:45.The midterm exam will be held on Oct. 29th, the final exam on Dec. 10th, both online.Additional details and updates will be provided in Moodle.- Donnerstag 01.10. 11:30 - 13:00 Digital (Vorbesprechung)
- Donnerstag 22.10. 11:30 - 13:00 Digital
- Donnerstag 29.10. 11:30 - 13:00 Digital
- Donnerstag 05.11. 11:30 - 13:00 Digital
- Mittwoch 11.11. 16:45 - 20:00 Digital
- Donnerstag 03.12. 11:30 - 13:00 Digital
- Donnerstag 10.12. 11:30 - 13:00 Digital
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
20 % homework
40 % midterm exam (online) (Oct, 29th, 2020)
40 % final exam (online) (Dec, 10th, 2020)
40 % midterm exam (online) (Oct, 29th, 2020)
40 % final exam (online) (Dec, 10th, 2020)
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
- Graphical solution method
- The Simplex algorithm
- 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 (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
- Graphical solution method
- The Simplex algorithm
- 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 (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: Fr 12.05.2023 00:13
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 as slides with audio comments. Homework examples have to be solved individually and have to be uploaded in Moodle. There will be tutorials for implementing simple LP models in Mosel, on the one hand online, where an live illustration of an implementation will take place and on the other hand in the PC Lab where students can practice their implementation skills under supervision.