CIF-MEREC: A Novel Algorithm Integrating Circular Intuitionistic Fuzzy Sets and MEREC for Multi-Criteria Decision-Making

Abstract

Multi-criteria decision-making (MCDM) under collective expert uncertainty requires methodscapable of capturing both individual concordance and inter-evaluator disagreementwhile deriving objective criterion weights from the data. Existing Circular IntuitionisticFuzzy (CIF) approaches have demonstrated the value of circular intuitionistic fuzzyrepresentations in group decision contexts; however, all rely on subjectively elicited criterionweights, limiting result reproducibility and introducing evaluator bias into the mostdecision-sensitive component of the process. This paper proposes CIF-MEREC, a novelhybrid algorithm that integrates Circular Intuitionistic Fuzzy Sets with the Method basedon the Removal Effects of Criteria (MEREC) for objective, data-driven criterion weightingin multi-criteria group decision-making. Expert evaluations are aggregated via the IntuitionisticFuzzy Weighted Averaging (IFWA) operator, preserving algebraic closure withinthe intuitionistic fuzzy space. A circular radius quantifying inter-evaluator dispersion ispropagated into a final score function S(λ), where λ ∈ [0, 1] parameterizes the decisionmaker’sattitude toward uncertainty. Criterion weights are derived exclusively throughMEREC by measuring the effect of removing each criterion from the evaluation system,eliminating any subjective intervention in the weighting process. The algorithm is assessedas a methodological proof of concept against three published CIF-based studies, namelyCIF-TOPSIS, CIF-VIKOR and CIF-ELECTRE III, using Spearman (ρ) and Kendall (τ) rankcorrelation coefficients across sixteen alternatives.