qR-TOPSIS — q-Rung Orthopair extension of TOPSIS
QR-TOPSIS (qR-TOPSIS — q-Rung Orthopair extension of TOPSIS) 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-TOPSIS lifts crisp TOPSIS (Hwang-Yoon 1981) into q-Rung Orthopair Fuzzy arithmetic. Weighted q-ROF matrix is built via λα operation (Liu-Wang 2018). PIS/NIS are extracted coordinate-wise per criterion direction. Separation from ideals uses standard q-ROF Minkowski/Euclidean distance with (μ^q, ν^q, π^q) components (Du 2018 style). Final ranking is by closeness coefficient CC_i = d⁻/(d⁺+d⁻), descending. No defuzzification step — distances are crisp scalars produced by the q-ROF distance function.
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-TOPSIS. ScholarGate. https://scholargate.app/decision-making/qr-topsis