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qR-CODAS — q-Rung Orthopair extension of CODAS

OriginatorNaz, S., Akram, M., Sattar, A., Al-Shamiri, M. M. A. (2022, AIMS Math 7(9):17529-17569) — 2TLq-ROF CODAS family-adjacent variant (closest verified application paper) Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016, Economic Computation 50(3):25-44) — crisp CODAS skeleton + τ=0.02 convention Liu, P. & Wang, P. (2018, IJIS 33:259-280) — q-ROFWA / q-ROFWG aggregation operators Du, W. S. (2018, IJIS 33(4):802-817) — Minkowski-type q-ROF distance measures (Euclidean + Hamming) Yager, R. R. (2017, IEEE TFS 25:1222-1230) — foundational q-Rung Orthopair Fuzzy SetYear2022Sources1Related methods8

QR-CODAS (qR-CODAS — q-Rung Orthopair extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Naz, S., Akram, M., Sattar, A., Al-Shamiri, M. M. A. (2022, AIMS Math 7(9):17529-17569) — 2TLq-ROF CODAS family-adjacent variant (closest verified application paper) Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016, Economic Computation 50(3):25-44) — crisp CODAS skeleton + τ=0.02 convention Liu, P. & Wang, P. (2018, IJIS 33:259-280) — q-ROFWA / q-ROFWG aggregation operators Du, W. S. (2018, IJIS 33(4):802-817) — Minkowski-type q-ROF

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-CODAS extends CODAS to q-ROFN inputs. Step 1 applies q-ROFN complement for cost criteria and Liu-Wang 2018 q-ROFWA per-criterion weighting; q-ROFN tuples persist through Step 2 (coordinate-wise NIS). Step 3 reduces q-ROFN to crisp scalars via Du 2018 q-ROF Euclidean (E_i) and Hamming (T_i) distances from the NIS. Step 4 builds the relative assessment h_ik with the standard CODAS threshold function ψ (default τ = 0.02). Step 5 sums h_ik into AS_i and ranks descending. NO score-function defuzzification is performed — the distance step is the natural q-ROFN → crisp reduction in CODAS.

Strengths & limitations

Strengths
  • 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.
Limitations
  • Assumes full compensation — a strong score on one criterion can offset a weak score on another.

Common pitfalls

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Sources

  1. 1.
    Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems
    DOI 10.1109/TFUZZ.2016.2604005

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ScholarGate. (2026, June 2). QR-CODAS. ScholarGate. https://scholargate.app/decision-making/qr-codas

qR-CODAS — qR-CODAS — q-Rung Orthopair extension of CODAS