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Outranking
qR-PROMETHEE - q-Rung Orthopair extension of PROMETHEE
q-Rung Orthopair outranking/ranking - q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)
Akram, M., Shumaiza2021doi:10.22111/IJFS.2021.6258 ↗
Overview
QR-PROMETHEE runs PROMETHEE II on q-rung orthopair ratings. Each (mu, nu) cell is turned into a crisp score s = (1 + mu^q - nu^q)/2, the usual criterion preference function compares alternatives pairwise on those scores, and the weighted preference index feeds the outgoing, incoming and net outranking flows. Rank by net flow, larger is better.
- Output
- preference flow, higher is better
- Data
- Q-Rung Orthopair Fuzzy, uncertainty tuples complete
- Weights
- Needs a weight source
- Size
- 2+ alternatives, 3-10 criteria works best
- Used for
- Q-Rung Orthopair Fuzzy MCDM, MAGDM under epistemic uncertainty, expert-driven evaluation with linguistic terms
Look elsewhere when
- •Small dataset (m<3) - outranking machinery underutilised
Assumptions to verify
- Thresholds (q, p, v) can be expressed in q-Rung Orthopair Fuzzy scale
- Veto + concordance semantics adapted to fuzzy arithmetic
Edge cases and pitfalls
Every cell must satisfy mu^q + nu^q <= 1 for the rung q you declare; a matrix that is valid at q=3 can be invalid at q=1.
The usual criterion is a step function: any positive deviation, however small, counts as full preference. Rounded input can flip a cell from 0 to 1.
Works with
Commonly takes its weights from
How to cite
Akram, M.; Shumaiza (2021). Multi-criteria decision making based on q-rung orthopair fuzzy promethee approach. Iranian Journal of Fuzzy Systems. https://doi.org/10.22111/IJFS.2021.6258
System ID, as it appears in reports and the API
QR-PROMETHEE