Intuitionistic Fuzzy ARAS
IF-ARAS (Intuitionistic Fuzzy ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. 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
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How it works
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When to use it
IF-ARAS extends crisp ARAS to settings where each rating is an Intuitionistic Fuzzy Number (μ, ν). The algorithm (i) IF-complements cost-criteria cells, (ii) constructs an optimal row R_0 in IFN space, (iii) defuzzifies each cell via Chen-Tan score s = μ − ν, shifts by +1, and column-sum-normalises across all m+1 rows, (iv) computes weighted sum S_i and the utility ratio K_i = S_i / S_0. Higher K_i = better. K_i ∈ [0, 1] when R_0 dominates every alternative cell-wise (the typical case). Algorithm is INVARIANT across pure IF-ARAS application papers (Mishra 2020 IT personnel selection, Mishra 20
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.
Common pitfalls
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Sources
- 1.Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems
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ScholarGate. (2026, June 2). IF-ARAS. ScholarGate. https://scholargate.app/decision-making/if-aras