A MAGDM method based on signed distance and improved HTFWA operator for green electricity retailer selection

As public environmental awareness grows, consumers are increasingly concerned about the green attributes of electricity. This paper develops a new hesitant triangular fuzzy multi-attribute group decision-making (MAGDM) method to study green electricity retailer selection. First, the hesitant triangular fuzzy hesitancy degree is defined according to the deviation between elements, the lower and upper-value deviation of the triangular fuzzy number, and the number of elements. On this basis, the hesitant triangular fuzzy signed distance (HTFSD) is defined, and two fundamental properties are proved respectively. Second, an attribute weight determination model is constructed based on the HTFSD and the idea of deviation maximization. Then, an improved hesitant triangular fuzzy weighted average (HTFWA) operator is proposed based on the psychological behavior of managers desiring larger benefits. Finally, a MAGDM method based on the HTFSD and the improved HTFWA operator is proposed and applied to green electricity retailer (ER) selection. The results indicate that: (1) Consumers give higher ratings to ERs with higher green levels, even at higher prices. (2) The proposed method effectively avoids the subjective impact of decision-makers artificially adding elements, making the weight results more aligned with actual survey findings while helping electricity consumers reasonably and objectively choose the optimal ER.