A similarity measure-based Pythagorean fuzzy additive ratio assessment approach and its application to multi-criteria sustainable biomass crop selection

Abstract

In the modern world, the selection of an optimal sustainable biomass crop type for biofuel production can be assumed as a multi-criteria decision-making problem due to numerous conflicting criteria. Therefore, uncertainty usually arises in sustainable biomass crop selection problems, and the Pythagorean fuzzy set, an extension of the intuitionistic fuzzy set, has been shown as a prolific tool to tackle uncertain and ambiguous information. Thus, the aim of the paper is to introduce an extended additive ratio assessment methodology to assess the sustainable biomass crop selection problem on Pythagorean fuzzy sets. In this method, a new Pythagorean fuzzy similarity measure is proposed. Afterward, a weighting procedure is developed based on the introduced similarity measure and score function to compute the criteria significance degrees. The computational process of the developed framework is illustrated using a case study of sustainable biomass crop selection with uncertain information, which verifies the feasibility and efficacy of the developed approach. Further, this work makes comparative and sensitivity analyses to confirm the steadiness and robustness of the presented method. The results illustrate that the developed method is a useful, practical, and valuable way to rank the sustainable biomass crop alternatives with uncertainty.