MCDMDecision-makingRankingMath steps
Hesitant Fuzzy Multi-Attributive Border Approximation area Comparison
HF-MABAC (Hesitant Fuzzy Multi-Attributive Border Approximation area Comparison) is a ranking multi-criteria decision-making (MCDM) method introduced by Mishra, A.R., Saha, A., Rani, P., Pamucar, D., Dutta, D., Hezam, I.M. in 2022. 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-MABAC 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., Saha, A., Rani, P., Pamucar, D., Dutta, D., Hezam, I.M. (2022). Sustainable supplier selection using HF-DEA-FOCUM-MABAC technique: a case study in the Auto-making industry. Soft Computing
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Cite this page
ScholarGate. (2026, June 2). HF-MABAC. ScholarGate. https://scholargate.app/decision-making/hf-mabac