Parameter reduction analysis under interval-valued m-polar fuzzy soft information

Abstract This paper formalizes a novel model that is able to use both interval representations, parameterizations, partial memberships and multi-polarity. These are differing modalities of uncertain knowledge supported by many models in the literature. The new structure embraces all these features simultaneously called interval-valued multi-polar fuzzy soft set (IV m FSS, for short). An enhanced combination -polar F) sets produces this model. As such, theory IV FSSs constitutes an multipolar-fuzzy generalization theory; multipolar theory. Some fundamental operations FSSs, including intersection, union, complement, “OR”, “AND”, explored investigated through examples. algorithm developed solve decision-making problems having data form. It applied two numerical In addition, three parameter reduction approaches their algorithmic formulation proposed FSSs. They respectively based on optimal choice, rank reduction, normal reduction. Moreover, outcomes compared with existing methods; relatedly, comparative analysis among investigated. Two real case studies selection best site airport construction rotavator studied.