Cubic-EDAS — Cubic Pythagorean Fuzzy EDAS (CuP-EDAS)
CUBIC-EDAS (Cubic-EDAS — Cubic Pythagorean Fuzzy EDAS (CuP-EDAS)) is a ranking multi-criteria decision-making (MCDM) method introduced by Paul, T.K., Jana, C., Pal, M. in 2023. 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
CuP-EDAS extends EDAS into the Cubic Pythagorean Fuzzy environment. Each decision-maker assessment is a CuPyFN = (⟨[Y⁻,Y⁺],[F⁻,F⁺]⟩,⟨Y,F⟩) with Pythagorean constraint (Y⁺)²+(F⁺)² ≤ 1 (stronger than intuitionistic). Expert opinions are aggregated via CuPyFWG. Distance-based optimization determines criteria weights. PDA/NDA use score function comparison vs average solution (avoids ideal solution bias). Higher appraisal score AS = (NS⁺+NS⁻)/2 is better.
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.Paul, T.K., Jana, C., Pal, M. (2023). Multi-criteria group decision-making method in disposal of municipal solid waste based on cubic Pythagorean fuzzy EDAS approach with incomplete weight information. Applied Soft Computing
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Cite this page
ScholarGate. (2026, June 2). CUBIC-EDAS. ScholarGate. https://scholargate.app/decision-making/cubic-edas