A multi-criteria group decision-making approach based on revised distance measures under dual hesitant fuzzy setting with unknown weight information

This study aims to revise the existing distance and similarity measures of the dual hesitant fuzzy set (DHFS) context in order to remove their shortcomings. The study first reveals the limitations of the existing distance measures of DHFSs through some counterexamples. Keeping in mind the indicated limitations of the existing ones, we revise them and show how the revised measures are important by applying them to the pattern recognition problem. Further, the statistical variance (SV) method is extended to the DHFS context for criteria weight determination. After that, an approach is made based on revised distance measures for ranking alternatives in multi-criteria group decision making (MCGDM). In the end, a real-world example regarding the evaluation of the quality of movies is presented to elaborate on the practicality and superiority of the developed approach.

Automated summary

This study aims to revise the distance and similarity measures of dual hesitant fuzzy sets (DHFS) to address their limitations, applying them to pattern recognition and multi-criteria group decision-making. The study also extends the statistical variance method to DHFS and proposes a revised distance measure method.