CORE Module “Planning and Optimization”

This module is focused on developing the mathematical and engineering skills required to plan for and optimize complex systems such as Intelligent Mobile Systems. It contains two courses on optimization: one focusing on quantitative methods and techniques for effective decision making, and the other dedicated to broader optimization problems, covering topics such as Lagrange multipliers, convex, and nonlinear programming. A third course focuses on planning and decision-making algorithms for autonomous systems.

Operations Research

Operations research is an interdisciplinary mathematical science that focuses on the effective use of technology by organizations. By employing techniques such as mathematical modeling, statistical analysis, and mathematical optimization, operations research finds optimal or near-optimal solutions to complex decision-making problems. Operations Research is concerned with determining the maximum (of profit, performance, or yield) or the minimum (of loss, risk, or cost) of some real-world objective. This course introduces students to modelling of decision problems and the use of quantitative methods and techniques for effective decision-making. Familiarity with a programming language (e.g., Python, C++, etc.) is desirable for this course.

  • Semester: Fall (3rd semester)
  • ECTS: 5
  • Instructor: Prof. Dr. Julia Bendul / Prof. Dr. Marcel Oliver

Artificial Intelligence

Artificial Intelligence is an important sub-discipline of Computer Science that deals with technologies to carry out tasks in an automated way that are usually associated with intelligence. AI methods have a significant application potential as there is an increasing interest and need to generate artificial systems that can carry out complex missions in unstructured environments without permanent human supervision. The course teaches a selection of the most important methods in AI. In addition to general purpose techniques and algorithms, it also includes aspects of methods that are especially targeted for physical systems like intelligent mobile robots or autonomous cars.

  • Semester: Fall (3rd semester)
  • ECTS: 5
  • Instructor: Prof. Dr. Andreas Birk, Dr. Szymon Krupinski


Optimization is a key step in the design of systems and processes. The course starts with classical search techniques like bisection, golden section, Newton’s algorithm, and conjugate gradient. It then discusses constrained problems like linear and quadratic programming based on the Lagrange formalism, and gives a first introduction to the concepts of convex optimization, in particular convex sets, convex functions, optimality conditions and duality. The course comes with a wide variety of examples and applications.

  • Semester: Spring (4th semester)
  • ECTS: 5
  • Instructor: Dr. Mathias Bode