Pythagorean Fuzzy Multi-Criteria Decision Making Method Based on Multiparametric Similarity Measure

Big data industry decision is supremely important for companies to boost the efficiency of leadership, which can vastly accelerate industrialized. With regard to big data industry decision assessment, the intrinsic problem involves enormous inexactness, fuzziness and ambiguity. Pythagorean fuzzy sets (PFSs), managing the uncertainness depicted in non-membership with membership, are a quite practical way to capture uncertainness. Firstly, the innovative Pythagorean fuzzy score function is given to dispose the comparison issue. Innovative distance measure and similarity measure for PFSs with three parameters are explored, along with corresponding proofs therewith. Later, objective weight is ascertained by deviation-based method. Also, combined weight is skillfully designed, which can tellingly imply both subjective preference and objective preference. In addition, an approach to settle Pythagorean fuzzy problem by multiparametric similarity measure is presented. The efficacy of developed algorithm is elaborated by a big data industry decision issue. Moreover, a comparison of the introduced algorithm with the selected existing methods has been built on the basis of the division by zero issue and counterintuitive phenomena for displaying its effectiveness.

Automated summary

This paper introduces the application of Pythagorean fuzzy sets in big data industry decision-making, including scoring functions, distance measures, similarity measures, and weight determination. The fuzzy problem is solved through multiparametric similarity measure, and the effectiveness of the algorithm is elaborated.