A Hybrid Best-Worst Method and TOPSIS Methodology for Multi-Criteria Hospital Ranking in Neutrosophic Settings

Abstract

This study presents a hybrid decision-making framework for hospital ranking that combines the Best–Worst Method (BWM), Single-Valued Neutrosophic Sets (SVNS), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Unlike previous research that combined BWM with conventional TOPSIS, this study innovatively applies BWM alongside SVN–TOPSIS, a less explored method for ranking hospitals. Data were collected from hospital administrators based on criteria including service quality, infrastructure, readiness of staff and equipment, payment options, reliability, and affordability. The BWM was employed to determine consistent weights for these criteria, SVNS addressed uncertain or incomplete data, and TOPSIS was used to derive the rankings. Sensitivity analysis with weight variations between 5% and 30% demonstrated the robustness of the rankings, validated by a Spearman’s rank correlation coefficient of 0.974. The findings confirm that this method is dependable and has potential applications in other uncertain decision-making contexts.