A social trust network-based hybrid fuzzy multi-criteria group decision-making approach for decarbonized and sustainable supplier selection

Selecting suitable suppliers is a critical decision for organizations, and multi-criteria group decision-making (MCGDM) has been proven as an effective technique in selecting the most appropriate supplier. However, most existing supplier selection methods do not fully explore the trust relationships among decision-makers (DMs), fail to effectively tackle complex and uncertain information, and neglect the interdependencies among criteria. To overcome these obstacles, we develop a social trust network (STN) based hybrid supplier selection approach that adopts the best-worst method (BWM) and the preference ranking organization method for enrichment evaluation (PROMETHEE) under interval-valued Pythagorean fuzzy (IVPF) environment. The main contributions are as follows: Firstly, a novel STN is constructed and extended to the IVPF setting to fully capture both direct and indirect trust relationships among DMs. Secondly, a new trust propagation operator is designed to determine DMs’ weights more objectively. Thirdly, the BWM method, known for its high consistency and efficiency, is adapted to the IVPF environment to derive optimal criterion weights and the preference relation-based PROMETHEE method is also integrated to rank the candidate suppliers. Fourthly, the IVPF weighted Hamy mean (IVPFWHM) operator is employed to aggregate DMs’ preferences and evaluations while explicitly capturing the interrelationships among criteria. The approach is practically applied to a real-world decarbonized and sustainable supplier selection case to validate the efficacy in complex and uncertain group decision-making scenarios while incorporating social trust dynamics. The sensitivity and comparison analyses are also conducted to validate the robustness and benefits of the method.