Rough-MABAC — Rough extension of MABAC
ROUGH-MABAC (Rough-MABAC — Rough extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Jia, F., Liu, Y., Wang, X. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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When to use it
ROUGH-MABAC (Jia et al. 2019) uses Intuitionistic Fuzzy Rough Numbers (IFRN=[(μ_l,ν_l),(μ_u,ν_u)]), NOT plain rough intervals. Pipeline: (1) for cost criteria swap μ↔ν (normalization); (2) IFWA scalar power w_j⊗β_ij=(1-(1-μ)^{w_j}, ν^{w_j}) applied to each IFN component; (3) IFRG geometric mean gives IFRBAA [g_j] per criterion; (4) signed Euclidean distance d_ij from IFRBAA: positive if [γ_ij]>[g_j] (via Definition 5 Sum-based comparison), negative if below; (5) Q_i=Σ_j d_ij, rank descending. No midpoint defuzzification — ranking is purely score-based via Euclidean distances.
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.
Sources
- Jia, F., Liu, Y., Wang, X. (2019). An extended MABAC method for multi-criteria group decision making based on intuitionistic fuzzy rough numbers. Expert Systems with Applications DOI: 10.1016/j.eswa.2019.03.016 ↗
How to cite this page
ScholarGate. (2026, June 2). Rough-MABAC — Rough extension of MABAC. ScholarGate. https://scholargate.app/en/decision-making/rough-mabac
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