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.
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
This section is available to Pro members. Upgrade to Pro
How it works
This section is available to Pro members. Upgrade to Pro
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.
Common pitfalls
This section is available to Pro members. Upgrade to Pro
Sources
- 1.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)
You have read it. What now?
Cite this page
ScholarGate. (2026, June 2). ROUGH-ARAS. ScholarGate. https://scholargate.app/decision-making/rough-aras