MCDMDecision-makingRankingMath steps
Spherical extension of EDAS
SF-EDAS (Spherical extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Garg, H., Sharaf, I.M. in 2022. 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
sf-edas extends EDAS to handle Spherical uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1) algebra. The final scores are defuzzified via score function S = μ² − ν² before ranking.
Strengths & limitations
Strengths
- 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.
Limitations
- 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.Garg, H., Sharaf, I.M. (2022). A new spherical aggregation function with the concept of spherical fuzzy difference for spherical fuzzy EDAS and its application to industrial robot selection. Computational and Applied Mathematics
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Cite this page
ScholarGate. (2026, June 2). SF-EDAS. ScholarGate. https://scholargate.app/decision-making/sf-edas