A simulation optimisation on the hierarchical health care delivery system patient flow based on multi-fidelity models

The mismatching patient flow distribution in the health care system in urban China is a great social issue that attracts lots of public attention. In this research, we propose a simulation-based optimisation method using the multi-fidelity optimisation with ordinal transformation (OT) and optimal sampling (OS) (MO2TOS$ \mathrm MO ^2\mathrm{TOS} $) algorithm to evaluate the patient flow distribution, so as to continuously improve the hierarchical health care service system. The low-fidelity model applying the queueing network theory is constructed for the OT part of the MO2TOS$ \mathrm MO ^2\mathrm{TOS} $, followed by a high-fidelity but time-consuming discrete event simulation model for the OS part. An empirical study on the background of the hierarchical health care delivery system in China is presented, where the proposed MO2TOS$ \mathrm MO ^2\mathrm{TOS} $ method is implemented to optimise the system profit by guiding the patient flow distribution. A comparison with other widely used simulation optimisation methods sustains the efficacy of the MO2TOS$ \mathrm MO ^2\mathrm{TOS} $ with the evidence that acquiring effective information from the low-fidelity model indeed retrenches the computing budget used to explore the feasible domain.