# 040897 KU LP Modeling II (MA) (2019S)

Continuous assessment of course work

## Labels

## Registration/Deregistration

- Registration is open from
**Mo 11.02.2019 09:00**to**We 20.02.2019 12:00** - Registration is open from
**Tu 26.02.2019 09:00**to**Tu 14.05.2019 12:00** - Deregistration possible until
**Tu 14.05.2019 23:59**

## Details

max. 30 participants

Language: English

### Lecturers

### Classes (iCal) - next class is marked with N

Thursday
09.05.
09:45 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Thursday
16.05.
09:45 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Thursday
23.05.
09:45 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Thursday
06.06.
09:45 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Thursday
06.06.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Thursday
13.06.
09:45 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Thursday
13.06.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Tuesday
18.06.
09:45 - 11:15
Hörsaal 9 Oskar-Morgenstern-Platz 1 1.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 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 at the end of the course.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

5 % presentation (date: June 13th, 2019)

40 % final exam (closed book; on paper) (date: June 18th, 2019)

30 % group homework

5 % presentation (date: June 13th, 2019)

40 % final exam (closed book; on paper) (date: June 18th, 2019)

### 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%

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-sizingFurthermore, 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.

- Transportation / assignment problems

- Transshipment and warehouse location problems

- Vehicle routing problems

- Network flow problems

- Network design problems

- Scheduling

- Lot-sizingFurthermore, 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.

* 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 15:29*