Generalized Dombi-based probabilistic hesitant fuzzy consensus reaching model for supplier selection under healthcare supply chain framework

Abstract

The process of supplier selection in supply chain management is a multi-criteria decision-making (MCDM) problem, characterized by several conflicting criteria. Despite the extensive research on MCDM in supplier selection, the healthcare sector has received limited attention compared to clinical operations research. A significant hurdle in evaluating healthcare supplier selection lies in handling uncertain information, characterized by probabilistic hesitant fuzzy (PHF) data that involves both hesitancy and probabilistic elements. Existing studies on PHF sets lack a versatile and comprehensive operator to enhance consensus and accommodate biased expert opinions. To address this gap, this study develops a model combining the merits of PHF sets, generalized Dombi operators, and a very popular MCDM method called measurement of alternatives and ranking according to compromise solution (MARCOS) for consensus-building in a group decision-making scenario. The proposed model provides higher degree of adaptability and flexibility in handling complexities in healthcare supplier selection problems. Within this model, calculation of consensus degree of each expert is facilitated through the application of correlation measures. To aggregate preferences of experts, PHF generalized Dombi weighted averaging aggregation operator has been introduced. The model further encompasses weights computation of the supplier selection criteria through an optimization model using cross-entropy and dispersion measures. A healthcare supplier selection problem is then considered to show the applicability of the developed model. Comparative analyses have been performed against existing methods to validate its robustness, ensuring consistency and effectiveness of the proposed model.