qR-EDAS — q-Rung Orthopair extension of EDAS
QR-EDAS (qR-EDAS — q-Rung Orthopair extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. 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
QR-EDAS follows Pattern B (Li-Wei-Lu 2019 + Liu-Wang 2018 + Keshavarz-Ghorabaee 2015 lineage): at F1 the q-ROFN matrix is reduced to crisp scores s_ij = μ_ij^q − ν_ij^q (Liu-Wang 2018 Def. 2.4). Cost criteria are handled at F1 by q-ROFN complement α^c=(ν,μ), equivalent to flipping the score sign. From F2 onwards the pipeline is identical to crisp EDAS (Keshavarz-Ghorabaee 2015): AV per criterion, PDA/NDA, weighted sums SP/SN, NSP/NSN normalisation, appraisal score AS=(NSP+NSN)/2. Rank descending by AS.
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.Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems
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Cite this page
ScholarGate. (2026, June 2). QR-EDAS. ScholarGate. https://scholargate.app/decision-making/qr-edas