Generalized Interval-Valued p,q-Rung Orthopair Fuzzy Decision-Making Model Integrating Correlation Analysis and Optimization Techniques
Keywords:
Correlation coefficie, Interval-valued information, Optimization, Statistical measureAbstract
Interval-valued p, q-rung orthopair fuzzy (IVpq-ROF) sets extend single-rung orthopair fuzzy models by allowing membership and non-membership grades to be governed by independent rung parameters, thereby widening the space of admissible expert judgements under uncertainty. This paper develops a statistical and decision-analytic framework for this dual-rung setting. Mean, variance, covariance, and correlation coefficients are formulated for IVpq-ROF numbers, and their fundamental properties—including symmetry, boundedness, and behaviour under scalar transformation—are established. Decision-maker weights are derived objectively from the correlation between each expert’s assessments and group-level outlier profiles, avoiding subjective prior weighting. A data envelopment analysis (DEA)-inspired linear programming model is then proposed to rank alternatives by optimising each alternative’s aggregated membership and non-membership degrees under a shared acceptability threshold, rather than using a fixed set of attribute weights. The resulting ranking is cross-validated through an integrated criteria importance through intercriteria correlation (CRITIC) and exponential Tomada de Decisão Interativa e Multicritério (TODIM) procedure, as well as through a Copeland-based fusion of individual decision-makers’ rankings. A sensitivity analysis examines how the ranking responds to independent variation in the two rung parameters. The proposed framework is illustrated through a supplier-type selection problem, and its structure is intended to be broadly applicable to multi-attribute group decision-making problems characterised by uncertain, interval-valued expert judgements.
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Copyright (c) 2026 Jawad Ali, Li Zou, Yan Shi (Author)
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