Hesitant Fuzzy Linguistic AHP (HFLTS-envelope family: Yavuz 2015 + Özdağoğlu 2018)
HFL-AHP (Hesitant Fuzzy Linguistic AHP (HFLTS-envelope family: Yavuz 2015 + Özdağoğlu 2018)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Yavuz, M., Öztaysi, B., Çevik Onar, S., Kahraman, C. in 2015. 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
Interval scores [a_i^-, a_i^+] describe the alternative's utility under pessimistic and optimistic readings of the same hesitant judgments. The preference-degree matrix P(A_i > A_j) ∈ [0,1] (with P(A_i>A_j) + P(A_j>A_i) = 1) compares two intervals; a round-robin majority on P gives the final ranking. Choose the 'preference_degree_yavuz' engine when intervals have non-zero width; the engine automatically falls back to midpoint comparison when all intervals collapse to points (degenerate-interval case).
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.
- 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.
Common pitfalls
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Sources
- 1.Yavuz, M., Öztaysi, B., Çevik Onar, S., Kahraman, C. (2015). Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model. Expert Systems with Applications
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Cite this page
ScholarGate. (2026, June 2). HFL-AHP. ScholarGate. https://scholargate.app/decision-making/hfl-ahp