MARCOS approach based upon cubic Fermatean fuzzy set and its application in evaluation and selecting cold chain logistics distribution center

Abstract

The evaluation and selection of cold chain logistics distribution center (CCLDC) possesses important strategic significance for enterprises to optimize logistics network. This paper firstly introduces an innovative uncertain information representation model called cubic Fermatean fuzzy set (CCFS) by integrating the cubic fuzzy set (CFS) and Fermatean fuzzy set (FFS) to portray the complicated indeterminacy and inaccuracy information. To begin with, the definition, score and accuracy functions, comparison laws, and generalized distance measure of CFFS are all defined successively. Then, based upon the defined Aczel-Alsina operations of CFFS, several cubic Fermatean fuzzy Aczel-Alsina aggregation operators are presented to flexibly integrate the cubic Fermatean fuzzy information, as well as some elegant properties of those operators are investigated at length. Again, two weight identification models are introduced based upon the defined score function and distance measure to calculate the weight of experts and criteria. In addition, an extended measurement alternatives and ranking based on the compromise solution (MARCOS) method by means of the presented operators, score function, and distance measure is brought forward within cubic Fermatean fuzzy circumstance. Consequently, an empirical study about the evaluation and selection of CCLDC is applied to demonstrate the efficacy and practicability of the presented method. Moreover, the comparative study and sensibility analysis are implemented to validate the rationality and superiority of the propounded method in addressing the problem of selecting the optimal CCLDC. The outcomes reflect that the developed approach provides a synthetic, robust and operable group decision framework for the evaluation and selection of CCLDC within an uncertain context.