Multiple fidelity simulation optimization of hospital performance under high consequence event scenarios

In optimizing systems, experimental models are often available with different levels of cost and different levels of "fidelity" or trustworthiness, a fact that can be exploited. For example, a highly detailed model might be made for a few possible configurations, supplemented by a large number of rough models that are less expensive to construct. The purpose of this paper is to illustrate the application of a recently proposed multiple fidelity sequential kriging optimization (MFSKO) method to derive the optimal resource allocation for disaster preparedness of a hospital. The system is evaluated via discrete event simulations of two sophistication levels. The MFSKO method integrates multiple fidelity data, including real-world data, in search for the global optima with less total evaluation cost. Kriging meta-models are generated as by-products of the optimization.