qR-CoCoSo — q-Rung Orthopair extension of COCOSO
QR-COCOSO (qR-CoCoSo — q-Rung Orthopair extension of COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Kuvvetli, B. İ. (2023, JESD 11(4):1294-1309) — first published q-ROF CoCoSo application Peng, X. & Huang, H. (2020, TEDE 26(4):695-724) — algorithm source for q-ROF score function with hesitancy penalty Yazdani, M., Zarate, P., Zavadskas, E. K., Turskis, Z. (2019, Mgmt Decision 57(9):2501-2519) — crisp CoCoSo skeleton Yager, R. R. (2017, IEEE TFS 25:1222-1230) — foundational q-Rung Orthopair Fuzzy Set in 2023. It turns a decision matrix of alternatives scored on
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-CoCoSo (Peng-Huang 2020 / Kuvvetli 2023). Each q-ROFN cell is reduced to a crisp score r_ij = μ^q − ν^q − ln(1 + π^q) that explicitly penalises hesitancy (Step 1). Scores are min-max normalised per criterion to [0, 1] with benefit/cost direction handled here (Step 2). Two compromise sequences are computed: S_i (weighted sum) and P_i (weighted-power sum). Three appraisal scores k_a (additive ratio), k_b (sum of min-ratios), k_c (balanced λ-ratio, default λ = 0.5) combine S and P. Final k_i = ∛(k_a·k_b·k_c) + (k_a+k_b+k_c)/3 is ranked descending. q parameter is analyst-specified (paper defaul
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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ScholarGate. (2026, June 2). QR-COCOSO. ScholarGate. https://scholargate.app/decision-making/qr-cocoso