MCDMDecision-makingRankingMath steps
Hesitant Fuzzy Linguistic Projection-Based MABAC with Bonferroni Mean (Sun et al. 2018)
HFL-MABAC (Hesitant Fuzzy Linguistic Projection-Based MABAC with Bonferroni Mean (Sun et al. 2018)) is a ranking multi-criteria decision-making (MCDM) method introduced by Sun, R., Hu, J., Zhou, J., Chen, X. 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
HFL-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.
Sources
- 1.Sun, R., Hu, J., Zhou, J., Chen, X. (2018). A Hesitant Fuzzy Linguistic Projection-Based MABAC Method for Patients' Prioritization. International Journal of Fuzzy Systems
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Cite this page
ScholarGate. (2026, June 2). HFL-MABAC. ScholarGate. https://scholargate.app/decision-making/hfl-mabac