The research group "Health Logistics" develops 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. This specificity of healthcare-scheduling
is precisely the subject of investigation of the MATH+ project AA3-6 (scheduling under restricted adaptivity).

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.