Fuzzy Multicriteria Decision-Making Methods
Summary
Fuzzy multicriteria decision-making integrates fuzzy logic into classical MCDM to manage uncertainty, imprecision and linguistic judgments inherent in real-world problems. Originating from Zadeh’s fuzzy sets, the field has expanded through intuitionistic and interval-valued extensions that capture both membership and non-membership degrees, as well as hesitation. Popular ranking techniques—such as fuzzy TOPSIS, VIKOR and COPRAS—combine membership functions with aggregation operators to prioritise alternatives. Sequential weighting schemes like SWARA and entropy-based methods ensure criteria reflect expert insights and data variability. Recent innovations introduce Z-numbers and q-rung orthopair fuzzy sets to model reliability alongside value estimates, enhancing robustness in contexts where expert confidence varies. Applications span energy sector performance evaluation, defence R&D project screening, healthcare risk management and sustainable supply-chain decisions. Contemporary research focuses on reducing computational complexity, avoiding rank reversal when sets of criteria or alternatives evolve, and developing hybrid models that marry multiple fuzzy constructs with classical decision rules to bolster both methodological rigor and practical usability.
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Research from all publishers
A new framework applies non-linear standardisation and a relative utility function to rank firms under economic, environmental, social and governance criteria. It achieves stable results free from rank reversal and adapts flexibly to market-driven weight variations. A comprehensive review of group decision-making methods presents a unified classification of expert-weighting techniques, categorising approaches by data type, ideal-solution concept and aggregation strategy, thereby offering a roadmap for both researchers and practitioners. In the defence sector, an interval-valued intuitionistic fuzzy VIKOR model has been devised to select R&D projects. By encoding preferences as interval-valued membership and non-membership degrees, it refines compromise ranking under deep uncertainty and captures expert hesitation more fully than earlier crisp or single-valued approaches.
Fuzzy Multicriteria Decision-Making Methods publication trend
The graph below shows the total number of articles in fuzzy multicriteria decision-making methods across all publications each year (not limited to Nature Index journals).
Technical terms
Multicriteria Decision-Making (MCDM): A structured approach for evaluating and ranking options against multiple, often conflicting, criteria.
Fuzzy Set: A mathematical structure where each element has a membership degree between 0 and 1, representing uncertainty.
Intuitionistic Fuzzy Set: An extension that assigns both membership and non-membership degrees plus a hesitation margin to each element.
Interval-Valued Intuitionistic Fuzzy Set: A further generalisation in which membership and non-membership are given as intervals to capture greater uncertainty.
Z-number: A pair of fuzzy numbers representing a quantitative value and its reliability, used to model expert confidence.
VIKOR Method: A compromise-based ranking technique that identifies solutions nearest to the ideal by balancing group utility and individual regret.
SWARA Method: A stepwise weighting procedure where experts sequentially assess the relative importance of criteria to derive weights.
References
- Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS) Method for Comparing Firm Performance in Energy Sector. Decision Making Advances (2024).
- A systematic review on multi-criteria group decision-making methods based on weights: Analysis and classification scheme. Information Fusion (2023).
- An Interval-Valued Intuitionistic Fuzzy VIKOR Approach for R&D Project Selection in Defense Industry Investment Decisions. Journal of Soft Computing and Decision Analytics (2024).
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