qR-ARAS — q-Rung Orthopair extension of ARAS
QR-ARAS (qR-ARAS — q-Rung Orthopair extension of ARAS) 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-ARAS extends ARAS to q-Rung Orthopair Fuzzy Numbers. Pipeline: (1) cost criteria are complemented via (μ,ν)→(ν,μ); (2) a fictitious optimal q-ROFN row A_0 is built coordinate-wise; (3) each row (including A_0) is aggregated by q-ROFWA into a single q-ROFN; (4) the q-ROFN is reduced to a crisp score S_i = (1+μ_i^q−ν_i^q)/2 ∈ [0,1]; (5) utility K_i = S_i/S_0 ∈ [0,1] is ranked descending. The q parameter (≥1) is analyst-specified; q=1 reduces to IF-ARAS, q=2 to PF-ARAS, q=3 to FF-ARAS.
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.
- 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-ARAS. ScholarGate. https://scholargate.app/decision-making/qr-aras