ORESTE methodology within a circular intuitionistic fuzzy framework for preferential outranking in hybrid cloud service selection

Abstract

This paper advances the ORESTE (Organísation, Rangement Et Synthèse de Données Relarionnelles) methodology within the Circular Intuitionistic Fuzzy (CIF) framework, highlighting its potential in practical decision analytics. The study first enhances CIF aggregation by employing the generalized mean technique, offering a flexible way to combine evaluative ratings and significance weights. Through modulation of the averaging parameter, decision-makers are able to accentuate either lower or higher values, thereby overcoming the constraints associated with conventional arithmetic means. The framework further improves decision precision through CIF similarity-driven appraisal indices, which utilize refined similarity metrics grounded in axiomatic properties such as symmetry, boundedness, identity, and monotonicity. These indices quantify the similarity between evaluative ratings and anchor references, while also revealing indifference and incomparability—thus equipping decision-makers with a comprehensive toolset for handling uncertainty. The CIF ORESTE framework comprises two methodologies. CIF ORESTE I delivers a global weak ranking using similarity-driven indices and generalized projection-related distances. CIF ORESTE II addresses the limitations of weak rankings by incorporating an Indifference-Preference-Incomparability (I-P-R) structure, which uses mean and net preference intensities to establish thresholds and clarify outranking relations. Applied to the evaluation of hybrid cloud services for a technology corporation, the CIF ORESTE framework demonstrates its effectiveness in resolving group decisions, managing uncertainty, and structuring preferences. Comparative analyses further underscore its robustness in handling CIF-based data and delivering reliable results.