Skip to content
Open access

A Systems-Based Superior–Committee Group Decision-Support Method Under Linguistic Intuitionistic Fuzzy Uncertainty

Aug 2026 · Systems · Vol 14, pp. 1035 · 0 citations · 42 references

TL;DR

A systems-based superior–committee group decision-support method for criteria weighting under linguistic intuitionistic fuzzy uncertainty provides a transparent mechanism for representing uncertain preferences, assigning expert influence, and deriving interpretable criteria weights in superior–committee group decision systems.

Abstract

Decision-making in organizational and socio-technical systems often involves a responsible superior expert who must integrate committee judgments expressed under linguistic uncertainty, hesitation, and heterogeneous expertise. This paper proposes a systems-based superior–committee group decision-support method for criteria weighting under linguistic intuitionistic fuzzy uncertainty. First, the best–worst method is extended by using linguistic intuitionistic fuzzy numbers to represent pairwise preference information with linguistic membership, non-membership, and indeterminacy degrees. A utility transformation is then introduced to convert linguistic intuitionistic fuzzy comparisons into numerical preference values, enabling criteria weights to be derived through linear programming models. Second, a two-stage group decision-support framework is developed for superior–committee decision structures. In the first stage, committee expert influence is calculated by integrating prior expert weights obtained from the superior expert’s evaluation with judgment-derived expert weights based on certainty and agreement. In the second stage, the final criteria weights are obtained by combining the superior expert’s judgments with the weighted committee judgments. A constructed UAV criteria-weighting case and complementary numerical analyses are presented to illustrate the calculation process and examine the behavior of the proposed method. The results show that the framework provides a transparent mechanism for representing uncertain preferences, assigning expert influence, and deriving interpretable criteria weights in superior–committee group decision systems.

Read PDF

Similar papers

Open access Oct 2026

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

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 circu...

Mayra Leticia Rodríguez-Carrillo, Ernesto León-Castro, L. Pérez-Domínguez et al. · 0 citations
Oct 2026

Integrating hesitant fuzzy linguistic term sets with ordinal priority approach: Application to digital supply chain supplier selection

Multi-criteria decision making problems often involve multiple decision makers who express their preferences using imprecise linguistic terms rather than exact numerical values. The Ordinal Priority Approach (OPA) derives weights directly from ordinal preferences, but its standard form assumes crisp ranks and canno...

G. Duman, Elif Kongar · 0 citations
Open access Oct 2026

Priorities for School Infrastructure Investment in the Face of Uncertainty: An Intuitionistic Fuzzy Multi-Criteria Decision-Making Method Employing the Frank Weighted Averaging Operator

There is a need for a framework of decisions that is able to deal with the uncertainty and subjective nature of expert judgment as well as limited public funds. This paper presents an integrated multi-criteria decision-making approach using Intuitionistic Fuzzy Numbers and the Frank Weighted Averaging operator to prior...

Muhammad Umar Mirza, Rabia Ali, R. Anjum et al. · 0 citations
Open access Sep 2026

Picture Fuzzy Multi-Attribute Decision-Making Approach with Dubois–Prade Aggregation in Industry 6.0

The proposed PFIDPA and PFIDPOWAA operators provide an effective framework for MADM problems involving uncertain, incomplete, and interrelated information and can serve as an efficient decision-support tool for Industry 6.0 adoption and other complex decision-making problems involving uncertain information.

K. Deva, A. J. Christilda, S. Manikandan et al. · 0 citations
Open access Oct 2026

An Uncertainty-Robust Decision Framework Based on Fractional Einstein Aggregation for Prioritizing Renewable Power Generation Technologies with Unknown Weights

Renewable power generation plays a central role in the transition toward sustainable, low-carbon energy systems. However, selecting an appropriate technology remains challenging because technical, economic, environmental, and operational criteria may conflict, while expert judgments may involve uncertainty and diversit...

Majed Albaity, Muhammad Rahim · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.