CEC2021 paper “Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic”

The paper “Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic”, which was written by Thomas Bartz-Beielstein, Marcel Droescher, Alpar Guer, Alexander Hinterleitner, Tom Lawton, Olaf Mersmann, Dessislava Peeva, Lennard Reese, Frederik Rehbach, Nicolas Rehbach, Amrita Sen, Aleksandr Subbotin, and Martin Zaefferer, has been accepted as a full paper at the IEEE Congress on Evolutionary Computation.

IEEE CEC 2021 is a world-class conference that brings together researchers and practitioners in the field of evolutionary computation and computational intelligence from around the globe. The Congress will encompass keynote lectures, regular and special sessions, tutorials, and competitions, as well as poster presentations. In addition, participants will be treated to a series of social functions, receptions, and networking events.

Abstract. Hospitals and health-care institutions need to plan the resources required for handling the increased load, i.e., beds and ventilators during the COVID-19 pandemic. BaBSim.Hospital, an open-source tool for capacity planning based on discrete event simulation, was developed over the last year to support doctors, administrations, health authorities, and crisis teams in Germany.

To obtain reliable results, 29 simulation parameters such as durations and probabilities must be specified. While reasonable default values were obtained in detailed discussions with medical professionals, the parameters have to be regularly and automatically optimized based on current data.
We investigate how a set of parameters that is tailored to the German health system can be transferred to other regions. Therefore, we use data from the UK. Our study demonstrates the flexibility of the discrete event simulation approach. However, transferring the optimal German parameter settings to the UK situation does not work—parameter ranges must be modified. The adaptation has been shown to reduce simulation error by nearly 70 %. The simulation-via-optimization approach is not restricted to health-care institutions, it is applicable to many other real-world problems, e.g., the development of new elevator systems to cover the last mile or simulation of student flow in academic study periods.