Dual Hesitant Fuzzy extension of EDAS
DHF-EDAS (Dual Hesitant Fuzzy extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Ning, B., Lin, R., Wei, G., Chen, X. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
Key highlights
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
Intuition
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How it works
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When to use it
DHF-EDAS (Ning et al. 2023) is the PDHF-MAGDM extension of EDAS: p decision-makers each supply a PDHF (Probabilistic Dual Hesitant Fuzzy) matrix, aggregated by PDHFWA (Eq.21); cost columns are complemented (Step 3); the score function s_{D_α}(Ʒ)=(1+s_ħ-s_λ)/2 (Eq.11) reduces each cell to a crisp value; the score matrix is min–max normalised per attribute (Eq.22); the combined attribute weight ϖ (Eq.20) fuses CRITIC objective weight ω, PDHF entropy weight η, and decision-maker subjective weight w via Lagrange relative-entropy minimisation; the PDHFEDAS pipeline (PDHFAV→PDA/NDA→SP/SN→NSP/NSN→PDH
Strengths & limitations
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
- May exhibit rank reversal when alternatives are added to or removed from the set.
- Assumes full compensation — a strong score on one criterion can offset a weak score on another.
Common pitfalls
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Sources
- 1.Ning, B., Lin, R., Wei, G., Chen, X. (2023). EDAS method for multiple attribute group decision making with probabilistic dual hesitant fuzzy information and its application to suppliers selection. Technological and Economic Development of Economy
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Cite this page
ScholarGate. (2026, June 2). DHF-EDAS. ScholarGate. https://scholargate.app/decision-making/dhf-edas