q-Rung Orthopair Fuzzy Decision-Making Methods
Summary
q-Rung orthopair fuzzy sets represent a significant evolution in fuzzy logic, generalising both intuitionistic and Pythagorean fuzzy sets by allowing the sum of the qth powers of membership and non-membership degrees to be at most unity. This enhancement offers decision analysts greater flexibility to express hesitation and uncertainty in complex environments. Central to q-rung orthopair fuzzy decision-making is the translation of qualitative judgements into quantitative frameworks via specialised operators—such as aggregation, similarity and distance measures—together with score and accuracy functions that facilitate ranking of alternatives. Over the last decade, scholars have proposed numerous multi-criteria decision-making (MCDM) algorithms integrating q-rung orthopair fuzzy information with established methods (for example TOPSIS, VIKOR, MARCOS, GLDS and CoCoSo). These hybrid approaches have demonstrated robust performance in domains as diverse as network cache placement, financial risk assessment, renewable energy evaluation and supply-chain management. The broad applicability of q-rung orthopair fuzzy decision-making derives from its capability to capture both positive and negative preferences simultaneously, to accommodate unbalanced data, and to reduce information distortion during aggregation. Recent advances have focused on extending these methods to interval-valued and complex-valued frameworks, introducing novel weighting schemes, and developing entropy-based and critic-based weight determination techniques. Collectively, this body of work underlines the global significance of q-rung orthopair fuzzy decision-making for problems characterised by deep uncertainty, multi-stakeholder involvement and the need for transparent, mathematically rigorous procedures.
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q-Rung Orthopair Fuzzy Decision-Making Methods publication trend
The graph below shows the total number of articles in q-rung orthopair fuzzy decision-making methods across all publications each year (not limited to Nature Index journals).
Technical terms
q-Rung Orthopair Fuzzy Set (q-ROFS): A fuzzy set characterised by a membership degree μ and non-membership degree ν satisfying μq + νq ≤ 1, offering greater expressive scope for hesitation.
Membership Degree: A measure between zero and one indicating the extent to which an element belongs to a fuzzy set.
Non-Membership Degree: A measure between zero and one indicating the extent to which an element does not belong to a fuzzy set.
Aggregation Operator: A mathematical function that combines multiple fuzzy values into a single representative value, preserving desired properties such as monotonicity and boundary conditions.
Score Function: A scalar mapping of a q-rung orthopair fuzzy number to a real value, used for ranking and comparison of alternatives.
Multi-Criteria Decision-Making (MCDM): A systematic approach for evaluating, ranking and selecting alternatives when multiple, often conflicting, criteria are involved.
References
- When content-centric networking meets multi-criteria group decision-making: Optimal cache placement policy achieved by MARCOS with q-rung orthopair fuzzy set pair analysis. Engineering Applications of Artificial Intelligence (2023).
- Fuzzy decision making method based on CoCoSo with critic for financial risk evaluation. Technological and Economic Development of Economy (2020).
- Generalized complex q-rung orthopair fuzzy Einstein averaging aggregation operators and their application in multi-attribute decision making. Complex & Intelligent Systems (2020).
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