MCDMRankinghesitant
Hesitant Fuzzy Additive Ratio Assessment
HF-ARAS (Hesitant Fuzzy Additive Ratio Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Mishra, A. R., Rani, P., Krishankumar, R., Ravichandran, K. S., Kar, S. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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When to use it
HF-ARAS ranks alternatives based on performance scores. Higher score = better rank.
Strengths & limitations
Strengths
- 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.
Limitations
- 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.
Sources
- Mishra, A. R., Rani, P., Krishankumar, R., Ravichandran, K. S., Kar, S. (2021). A multi-criteria framework for evaluating the sustainable drug selection for COVID-19 patients using hesitant fuzzy information and ARAS method. Applied Soft Computing Journal link ↗
How to cite this page
ScholarGate. (2026, June 2). Hesitant Fuzzy Additive Ratio Assessment. ScholarGate. https://scholargate.app/en/decision-making/hf-aras