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2021 IEEE Congress on Evolutionary Computation, CEC 2021 ; : 728-735, 2021.
Article in English | Scopus | ID: covidwho-1708826


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. © 2021 European Union

2021 Genetic and Evolutionary Computation Conference, GECCO 2021 ; : 293-294, 2021.
Article in English | Scopus | ID: covidwho-1341346


Pandemics pose a serious challenge to health-care institutions. To support the resource planning of health authorities from the Cologne region, BaBSim.Hospital, a tool for capacity planning based on discrete event simulation, was created. The predictive quality of the simulation is determined by 29 parameters with reasonable default values obtained in discussions with medical professionals. We aim to investigate and optimize these parameters to improve BaBSim.Hospital using a surrogate-based optimization approach and an in-depth sensitivity analysis. © 2021 Owner/Author.