A New Method for Multi-Criteria Decision-Making: Adaptive Ranking with Ideal Evaluation (ARIE)

Abstract

Decision makers frequently confront complex criteria, some requiring maximization, others minimization, and still others precise target attainment, yet classical Multi-Criteria Decision-Making (MCDM) methods (e.g., TOPSIS, VIKOR and SAW) offer limited flexibility to handle such mixed preference directions, often producing inconsistent or opaque rankings. To address these
challenges, this study proposed the Adaptive Ranking with Ideal Evaluation (ARIE), a fully flexible similarity-based framework that unifies benefit, cost, and target-type normalization under one roof. ARIE leverages a novel dual-parameter score function with a sensitivity exponent γ to control the nonlinearity of deviations and a balancing coefficient κ to tailor the trade-off between aspiration toward the ideal and avoidance of the anti-ideal to convert weighted normalized ratios into a single, interpretable closeness measure. We demonstrate ARIE in a case study of halal supplier selection, perform a sensitivity analysis between γ and κ values, and carry out comparative and simulation-based analyzes using MATLAB-generated weight scenarios against
seven benchmark methods (CRADIS, MABAC, ARAS, MOORA, VIKOR, TOPSIS and SAW). The results show that ARIE’s new scoring technique consistently yields more stable, transparent, and decision-maker-aligned rankings in diverse decision contexts.