MULTI-ATTRIBUTE DECISION MAKING BASED ON PROBABILISTIC DUAL HESITANT PYTHAGOREAN FUZZY INFORMATION

Abstract

In this paper, we propose a new multi-attribute decision-making (MADM) method based on probabilistic dual hesitant Pythagorean fuzzy (PDHPF) sets. The existing PDHPF power weighted Hamy mean operator has the drawback that if one PDHPF element among the PDHPF elements whose non-membership grade equals 0, then the non-membership grade of the aggregated PDHPF element is equal to 0. Thus, the existing MADM method based on the PDHPF power weighted Hamy mean operator has the drawback that it cannot distinguish the ranking orders of alternatives in some situations. To overcome these drawbacks of the existing MADM method, this paper proposes the PDHPF improved power weighted averaging (PDHPFIPWA) operator and proposes a MADM method based on the proposed PDHPFIPWA operator. The proposed MADM method can overcome the drawbacks of the existing MADM method. It offers us a very useful approach for MADM in PDHPF environments.