Universität Wien FIND

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040897 KU LP Modeling II (MA) (2020S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first serve).

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Due to the current situation, the course will rely on home learning. The dates for the group presentation / exam / homework will remain as originally planned but they will take place online via Moodle. Details will be available in Moodle.

Thursday 07.05. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Thursday 14.05. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Thursday 28.05. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Thursday 04.06. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 10.06. 09:45 - 11:15 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 10.06. 11:30 - 13:00 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 18.06. 09:45 - 11:15 Hörsaal 15 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 18.06. 09:45 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Thursday 18.06. 11:30 - 13:00 Hörsaal 15 Oskar-Morgenstern-Platz 1 2.Stock

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 Mosel/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.

Furthermore, there will be a group homework assignement where students need to understand, possibly adapt and implement an LP model from literature. There will be short presentations of the models during class.

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

25 % individual homework assignements
30 % group homework (due: July 15th, 2020 via Moodle)
5 % presentation (date: June, 10th, 2020 via Moodle)
40 % final exam (date: June 18th, 2020 via Moodle)

Minimum requirements and assessment criteria

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%

Examination topics

Students are expected to understand, formulate and solve a variety of LP models and implement them using Mosel / XpressMP. Slides will be available in Moodle.

Part of the exam will consist of the correct formulation and the understanding of models related to
- Transportation / assignment problems
- Transshipment and warehouse location problems
- Vehicle routing problems
- Network flow problems
- Network design problems
- Scheduling
- Lot-sizing

Furthermore, the exam will 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.

Reading list

* 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.

Association in the course directory

Last modified: Mo 07.09.2020 15:20