Dual Hesitant Fuzzy extension of TOPSIS
DHF-TOPSIS (Dual Hesitant Fuzzy extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, R., Li, W., Zhang, T., Han, Q. in 2020. 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-TOPSIS (Wang et al. 2020) extends classical TOPSIS to Dual Hesitant Fuzzy assessments. Operations are performed directly on DHFEs without length-equalisation: A⁺ and A⁻ are obtained from column-wise extrema of (h, g) components (direction-aware), the weighted generalised DHF distance d_wpg combines mean (G_m, G_n) and volatility (V_m, V_n) functions weighted by α and β (α+β=1), and alternatives are ranked in descending order of the closeness coefficient CC.
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.Wang, R., Li, W., Zhang, T., Han, Q. (2020). New Distance Measures for Dual Hesitant Fuzzy Sets and Their Application to Multiple Attribute Decision Making. Symmetry
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Cite this page
ScholarGate. (2026, June 2). DHF-TOPSIS. ScholarGate. https://scholargate.app/decision-making/dhf-topsis