A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown:...

COVID-19 pandemic has affected the entire world. During the Covid-19 pandemic, which is tried to be prevented by all countries of the world, regulations have been made to reduce the effect of the virus in sectors such as banking, tourism, and especially transportation. Social isolation is one of the most critical factors for people who have or are at risk of contracting COVID-19 disease. Many countries have developed different solutions to ensure social isolation. By applying lockdown for specific periods, preventing the movement of people will reduce the rate of transmission. However, some private and public institutions that have to serve during the lockdown period should be carefully determined. In this study, we aim to determine the petrol stations to serve during the COVID-19 lockdown, and this problem is handled as a multi-criteria decision-making problem. We extend the spherical fuzzy VlseKriterijumska Optimizacija IKompromisno Resenje (SF-VIKOR) method with the spherical fuzzy Analytic Hierarchy Process (SF-AHP). To show its applicability in complex decision-making problems, Istanbul is selected to perform a case study; thirteen petrol stations are evaluated as potential serving petrol station alternatives during the lockdown. Then, the novel SF-AHP integrated SF-VIKOR methodology is structured; the problem is solved with this methodology, and the best alternative is determined to serve in lockdown. Accessibility of the petrol station and Measures taken by station managers are determined to be essential for the effectiveness of the lockdown process. The neighborhood population and the station's proximity to hospitals are also critical inner factors to fight the pandemic. To test the methodology, Spherical Fuzzy the Weighted Aggregated Sum-Product Assessment (SF-WASPAS) is utilized. Public or private organizations can use the proposed methodology to improve their strategies and operations to prevent the spreading of COVID-19.