Rough-ARAS — Rough extension of ARAS
ROUGH-ARAS (Rough-ARAS — Rough extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Daoud Ben Amor, W., Moalla Frikha, H., Martínez López, L. in 2021. 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-ARAS (IRN-ELH-ARAS) extends ARAS to IRN uncertainty. Step 1 (F1): define optimal baseline X_0 via element-wise max/min. Step 2 (F2): normalise using ARAS sum-based formula; for cost criteria invert IRN as [1/U,1/L] (swap bounds) before summing. Step 3 (F3): weight normalised values with crisp or rough weights. Step 4 (F4): compute rough row sums S_i^{IRN}. Step 5 (F5): utility degree K_i = defuzz(S_i^{IRN} ⊘ S_0^{IRN}) — rough IRN division first, then midpoint defuzz. Rank descending by K_i.
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.
Sources
- Daoud Ben Amor, W., Moalla Frikha, H., Martínez López, L. (2021). The Interval Rough Number of the Extended ARAS Method for Solving Multi-Criteria Group Decision Making. 2021 International Conference on Decision Aid Sciences and Application (DASA) DOI: 10.1109/DASA53625.2021.9681928 ↗
How to cite this page
ScholarGate. (2026, June 2). Rough-ARAS — Rough extension of ARAS. ScholarGate. https://scholargate.app/en/decision-making/rough-aras
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