Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories:
 1215 ECTS in technical scientific modules (TSM)
TSM modules teach profilespecific specialist skills and supplement the decentralised specialisation modules.  912 ECTS in fundamental theoretical principles modules (FTP)
FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.  69 ECTS in context modules (CM)
CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.
In the module description (download pdf) you find the entire language information per module divided into the following categories:
 instruction
 documentation
 examination
This course offers an introduction to optimization, emphasizing basic methodologies and underlying mathematical structures. Optimization refers to the application of mathematical models and algorithms to decision making. A large number of quantitative realworld problems can be formulated and solved in this general framework. Applications of optimization comprise, for instance, decision problems in production planning, supply chain management, transportation networks, machine and workforce scheduling, blending of components, telecommunication network design, airline fleet assignment, and revenue management.
Prerequisites
Linear algebra:
 Systems of linear equations, Gauss algorithm
 Basics of vector and matrix algebra, linear spaces
Analysis:
 Calculus with functions of one variable
 Zeros of functions (Newton algorithm)
Programming:
 Basics of procedural programming and ability to implement small programs in an arbitrary language, e.g. Python, Matlab, R, Java, C#, C++, C, etc.
Learning Objectives
 The student has an overview of the various fields and approaches to optimization.
 The student has a basic mathematical and algorithmic understanding of the major optimization methods used in practice (Linear Programming (LP), Integer Programming (ILP), Nonlinear Programming, Optimization in Graphs, Metaheuristics).
 The student is able to analyze basic realworld decision problems and formulate appropriate optimization models.
 The student is able to implement and solve basic LP/ILP models in a spreadsheet.
 The student has developed a certain intuition on how to approach and analyze realworld optimization problems, to correctly estimate their complexity, and to choose appropriate modeling approaches and implementation tools.
Contents of Module
Week 
Topics 
1 
PART 1: Introduction to Optimization

2 

3 
Linear Programming

4 

5 

6 
Integer Programming

7 

8 
PART 2:

9 
Graphs and Networks

10 

11 

12 
Heuristics and Metaheuristics

13 

14 
Teaching and Learning Methods
Lectures and exercises
Download full module description
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