Enhancing pairwise comparisons for multi-criteria decision making: application to healthcare waste management

Abstract Accepted by: M. Zied Babai This paper introduces a new approach to group decision-making called the Belief Interval-Fuzzy Best-Worst Method (BI-FBWM). It builds on existing decision-making techniques by more effectively incorporating uncertainty and differing expert opinions. Although the proposed method is as simple to use as traditional approaches, it adds three key steps to enhance accuracy: calculating reference comparisons, constructing decision matrices to capture uncertainty, and applying a new mathematical model to assign importance to various criteria. The method was developed during the COVID-19 pandemic and applied to evaluate healthcare waste management practices. In addition, this study proposes a new assessment framework tailored to pandemic conditions, based on guidelines from the World Health Organization and national health authorities. Finally, comparative analysis demonstrates that the proposed methodology offers clear advantages in handling complex decision problems under uncertainty when compared with the original Best-Worst Method (BWM), FBWM and its variants within the D number environment.