General Information
Here you will find the lectures that are offered on a Bachelor level.
If you would like to attend a Seminar or write your Bachelor Thesis at our chair, please participate in the allocation procedure, organized by Dr. Jürgen Blank.
For further information please contact
Konstantin Kloster,
Marvin Caspar, or
Daniel Schermer to receive information about available topics.
Basics of Data Science
Management Science (Operations Research)
Content
- Management Science 1 (Operations Research 1):
- Introduction to Operations Research: planning models and methods.
- Networks, graphs, and their applications: Paths in graphs, networks, flows in graphs, transportation problem
- Linear Optimization: basic model structures, Simplex algorithm, special LP structures, different phases of Simplex, variables with lower and upper bounds, post-optimality analysis (sensitivity analysis), parametric optimization, applications and modeling, duality.
- Integer linear optimization: Examples and case studies, Branch & Bound, Cutting Planes.
- Stochastic Processes: Queueing theory, simulation of stochastic processes.
- Management Science 2 (Operations Research 2):
- Nonlinear Optimization: Unconstrained and constrained nonlinear problems and models, Convex optimization, Karush-Kuhn-Tucker-conditions, lagrangian method, Quadratic optimization (Wolfe’s Algorithm), approximation methods (Golden Section, Gradient Method), Barrier methods, Penalty methods.
- Heuristics: problem search methods (A*-algorithm), local search methods, Simulated Annealing, Genetic Algorithms.