Hesitant Fuzzy EDAS (Evaluation Based on Distance from Average Solution)
HF-EDAS (Hesitant Fuzzy EDAS (Evaluation Based on Distance from Average Solution)) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F.; Kahraman, C.; Civan, H. (HF-EDAS); Keshavarz Ghorabaee, M. et al. 2015 (base EDAS); Yu, D. 2014 (TFHFS value-space) in 2018. 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
HF-EDAS evaluates alternatives by their distance from the average solution under triangular fuzzy hesitant uncertainty. Higher Appraisal Score (AS ∈ [0,1]) indicates an alternative that exceeds the average in benefit criteria and stays below it in cost criteria. For group decisions, aggregate DM evaluations first via TFHFWA/TFHFWG with DM importance weights. Method 1 (defuzz-then-EDAS) is recommended for first-time users; Method 2 (fuzzy-throughout) preserves uncertainty granularity for sensitivity analysis.
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.Kutlu Gündoğdu, F., Kahraman, C., Civan, H. N. (2018). A novel hesitant fuzzy EDAS method and its application to hospital selection. Journal of Intelligent & Fuzzy Systems
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ScholarGate. (2026, June 2). HF-EDAS. ScholarGate. https://scholargate.app/decision-making/hf-edas