Hesitant extension of HF-CODAS
HF-CODAS (Hesitant extension of HF-CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Torra, V. in 2010. 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-codas extends HF-CODAS to handle Hesitant uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Hesitant Fuzzy Element (HFE: set of possible membership degrees) algebra. The final scores are defuzzified via envelope: (min + max)/2, or mean of all values before ranking.
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.
- Assumes full compensation — a strong score on one criterion can offset a weak score on another.
Common pitfalls
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Sources
- 1.Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems
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Cite this page
ScholarGate. (2026, June 2). HF-CODAS. ScholarGate. https://scholargate.app/decision-making/hf-codas