The health sector, and in particular the hospital public service, represents a very large part of total government expenses. Therefore, the research for more efficient clinical pathways has become a central question to ensure the quality of healthcare. A first step towards this goal was achieved during the last decade, as most hospitals started to collect both physiological data and process-related data at different stages of a patient's care journey. There is still an urge for algorithms that use this data in order to improve the efficiency of clinical resources, reduce the variability in the processes, and ultimately reduce the waiting times of patients before getting the adequate treatment.

In the group Mathematics of Healthcare, we develop innovative algorithms for the logistics in hospitals, while respecting the specificities of this sector (compared to other supply chain management problems in the industry). In particular, we must compute solution strategies that simultaneously optimize several criteria, such as patient waiting times and hospital revenues, while ensuring the stability of the process in case of unexpected events. Another critical issue is the acceptance of our approach by actors from the medical sector. This implies a close cooperation with medical doctors and hospital managers, such as our partners of the Charité hospital and the German organization for surgery
management (VOPM) in the project IBOSS.

From a mathematical point of view, we are concerned with scheduling problems under uncertainty and data ambiguity. Robust optimization plays a central role, as a stable planning reduces the organizational burden, which can have a very negative impact on the quality of healthcare. Our aim is to push forward the understanding of scheduling policies in an uncertain environment, to develop efficient algorithms that can be used by hospital managers, and ultimately to contribute to the data-revolution in the logistics of healthcare.