---

Linear stochastic bilevel problems -although explained quickly- pose some difficulties when it comes to solving, even without the stochasticity. The aim of this work is to find a technique that allows for the use of decomposition methods known from stochastic programming in the framework of linear stochastic bilevel problems. The uncertainty is modeled as a discrete, finite distribution on some probability space. Two approaches are made, one using the optimal value function of the lower level, whereas the second technique utilizes the Karush-Kuhn-Tucker conditions of the lower level. Using the latter approach, an integer-programming based algorithm for the global resolution of these problems is presented and evaluated.

---

Dr. Charlotte Henkel began studying mathematics at the Technische Universität Dortmund in 2004. In 2009, she completed with a Diploma and started her doctor thesis in 2010 at the chair for discrete mathematics and optimization at the University Duisburg-Essen. Since 2014, she is a Consultant in the banking sector.

---