Stochastic optimization combines the business activities of the ior/cf-HSG. Scientific research projects test discretization and approximation methods as well as efficient solution algorithms. The theoretical findings are incorporated into application-oriented research projects that focus on modeling real-world problems and developing appropriate software solutions.
These manage dynamic decision-making processes, which are largely determined by the uncertainty regarding the future development of relevant factors. Together with regulatory and corporate requirements, this leads to complex decision-making situations that generally overtax human intuition. With the help of stochastic optimization and modern time series analysis, these correlations can be modeled and decisions can be aligned in the best possible way with regard to predefined goals.