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Home›Decision-making›Hesitant Fuzzy Additive Ratio Assessment
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

  1. 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

Similar methods

HF-COPRASHF-WASPASDHF-COPRASHF-MABACHF-MOORAHF-EDASIF-ARASHF-GRA

Related reference concepts

Decision MakingDecision Support SystemsCriteria for Decision-Making under Risk and UncertaintyDecision Making SkillsParticipative Decision MakingEvaluation Criteria

Spotted an issue on this page? Report or suggest a fix →

ScholarGate — HF-ARAS (Hesitant Fuzzy Additive Ratio Assessment). Retrieved 2026-07-21 from https://scholargate.app/en/decision-making/hf-aras · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Mishra, A. R., Rani, P., Krishankumar, R., Ravichandran, K. S., Kar, S.
Subfamily
Ranking
Year
2021
Type
Hesitant fuzzy utility-degree ranker (P i / P 0 against ideal alternative row)
Value Space
hesitant
Uncertainty
epistemic
Compensation
full
Rank Reversal
Yes
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