Improving emergency departments during COVID-19 pandemic: a simulation and MCDM approach with MARCOS methodology in an uncertain environment

The coronavirus disease (COVID-19) pandemic forced healthcare systems to quickly modify to swapping healthcare essentials. The emergency department (ED) decision-making condition is complex and particularly unstable order for care in a stated period conducts decision-makers to attempt to alter assets to touch the demand. ED managers are generally enforced to discover strategies and improving scenarios for decreasing transfer of patients. For this end, the proposed framework of this study is first developed to integrate the simulation model of the flow process of the COVID-19 patients with the Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) methodology in Spherical fuzzy context to assess and prioritize scenarios based on desired performance measures. As a contribution, the proposed framework determined the importance of the performance measures based on Spherical fuzzy sets. The proposed SF-MARCOS approach takes the performance measures weights from the expert’s team based on spherical fuzzy theory and the performance measures values from the simulation model, and rank the improving scenarios. Finally, a real-life study in a private hospital in Tehran, Iran, illustrates the effectiveness and feasibility of the proposed framework. The analysis of the results shows that the patients’ transfer rate can be reduced by applying new strategies with sensible expenditure.