Optimization of High-dimensional Simulation Models Using Synthetic Data

Abstract: Simulation models are valuable tools for resource usage estimation and capacity planning. In many situations, reliable data is not available. We introduce the BuB simulator, which requires only the specification of plausible intervals for the simulation parameters. By performing a surrogate-model based optimization, improved simulation model parameters can be determined. Furthermore, a detailed statistical analysis can be performed, which allows deep insights into the most important model parameters and their interactions. This information can be used to screen the parameters that should be further investigated. To exemplify our approach, a capacity and resource planning task for a hospital was simulated and optimized. The study explicitly covers difficulties caused by the COVID-19 pandemic. It can be shown, that even if only limited real-world data is available, the BuB simulator can be beneficially used to consider worst- and best-case scenarios. The BuB simulator can be extended in many ways, e.g., by adding further resources (personal protection equipment, staff, pharmaceuticals) or by specifying several cohorts (based on age, health status, etc.). Keywords: Synthetic data, discrete-event simulation, surrogate-model-based optimization, COVID-19, machine learning, artificial intelligence, hospital resource planning, prediction tool, capacity planning.