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Beschreibung:
Data-driven decision-making holds great potential for increasing the productivity of companies and organizations. However, this potential is not yet fully leveraged as there are still certain barriers to implementing such systems in practice. This doctoral thesis presents three research papers following the same purpose: offering solution approaches for common challenges in the field of data-driven decision-making via novel optimization procedures. The three challenges under consideration are the (i) explainability, (ii) scalability, and (iii) accessibility of data-driven decision-making approaches. Besides individual contributions in the respective area of application, each paper targets one of the three challenges in particular. The first paper of this doctoral thesis develops an explainable data-driven algorithm for personalized medicine. In particular for off-policy learning, where the goal is to derive personalized treatment decisions based on individual patient characteristics from observational data, e.g., randomized control trials. The resulting treatment decisions can be presented in disjunctive normal form, i.e., OR-of-ANDs, and fulfill explainability demands from clinical practice. This is shown in a user study, in which we ask actual clinical practitioners to rate the interpretability of our approach. The main contribution, that makes this new algorithm possible, lies in the field of mathematical optimization. That is, a novel formulation of off-policy learning as a mixed-integer linear program, and a tailored column generation procedure within a branch-and-bound framework to solve it. The second paper proposes an efficient Monte Carlo tree search (MCTS) for data-driven dynamic police patrolling. Thereby, the goal is to optimize and dynamically adjust patrol routes of police units through their patrol beats, i.e., predefined patrol areas, with the aim of crime risk reduction. In contrast to state-of-the-art patrol algorithms based on vehicle routing problem formulations, the novel MCTS approach ...