Lehrinhalte
This course tackles essential and advanced concepts of model-based optimization. The class teaches how to model practical optimization problems efficiently and how to solve them using model-based techniques. Some topics are: basics of planning and modelling, advanced concepts of linear programming, integer programming, stochastic programming, and decomposition approaches.
Literatur
Williams, H.P. (2013): Model Building in Mathematical Programming, 5. Aufl., John Wiley & Sons, Chichester.
Voraussetzungen
Introduction to Operations Research
Further Grading Information
After the courses the students are able to
[list]
[*]assess the possible fields of application of model-based planning techniques.
[*]reflect and process the steps of model-based planning.
[*]use quantitative methods effectively.
[*]model and solve real-world problems using computational methods.
[*]judge the results of quantitative analyses and to communicate them orally and in written form correctly.
[/list]
Online-Angebote
moodle
This course tackles essential and advanced concepts of model-based optimization. The class teaches how to model practical optimization problems efficiently and how to solve them using model-based techniques. Some topics are: basics of planning and modelling, advanced concepts of linear programming, integer programming, stochastic programming, and decomposition approaches.
Literatur
Williams, H.P. (2013): Model Building in Mathematical Programming, 5. Aufl., John Wiley & Sons, Chichester.
Voraussetzungen
Introduction to Operations Research
Further Grading Information
After the courses the students are able to
[list]
[*]assess the possible fields of application of model-based planning techniques.
[*]reflect and process the steps of model-based planning.
[*]use quantitative methods effectively.
[*]model and solve real-world problems using computational methods.
[*]judge the results of quantitative analyses and to communicate them orally and in written form correctly.
[/list]
Online-Angebote
moodle
- Lehrende: Simon Emde
Semester: WT 2018/19