Research

The Chair of Management Science develops quantitative and data-driven methods to support complex decision-making in business and society. Our research combines Operations Research, Machine Learning, and Behavioral Insights to address real-world challenges in logistics, mobility, and healthcare. We study learning and optimization under uncertainty to improve routing, delivery, and workforce scheduling - particularly in urban logistics and home healthcare services. Central topics of our research include the development of data-driven stochastic optimization methods for complex dynamic decision problems, and the integration of human preferences and skills as well as fairness considerations into optimization models to achieve more efficient, equitable, and adaptive operations.