LPF-CRITIC-EDAS — Linguistic Pythagorean Fuzzy EDAS with CRITIC weighting (Akram-Ramzan-Deveci 2023)
LPF-EDAS (LPF-CRITIC-EDAS — Linguistic Pythagorean Fuzzy EDAS with CRITIC weighting (Akram-Ramzan-Deveci 2023)) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Ramzan, N., Deveci, M. in 1995 crisp; 2023 variant applicator. 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
LPF-EDAS uses linguistic Pythagorean fuzzy numbers (LPFN: (I_ψ, I_ζ) with ψ²+ζ² ≤ τ²) to encode expert linguistic judgments at granularity τ. Group judgments are aggregated via LPFHWA (Hamacher weighted average; algebraic at κ=1). CRITIC derives objective criterion weights from inter-criterion correlation and standard deviation. EDAS structure (PDA/NDA distances from average solution) produces the final appraisal score. The score function S(α) = √((τ²+ψ²−ζ²)/2) defuzzifies LPFNs throughout.
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.Akram, M., Ramzan, N., Deveci, M. (2023). Linguistic Pythagorean fuzzy CRITIC-EDAS method for multiple-attribute group decision analysis. Engineering Applications of Artificial Intelligence
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Cite this page
ScholarGate. (2026, June 2). LPF-EDAS. ScholarGate. https://scholargate.app/decision-making/lpf-edas