Spherical Fuzzy Z-Number MARCOS Ranking
SFZN-MARCOS (Spherical Fuzzy Z-Number MARCOS Ranking) is a distance multi-criteria decision-making (MCDM) method introduced by Niu, J. in 2024. 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
Output ranks alternatives by F(U_i) ∈ [0, 1] (MARCOS utility function combining utility to ideal and anti-ideal). Best alternative has highest F(U). MARCOS differs from TOPSIS by constructing an EXTENDED decision matrix that explicitly includes anti-ideal and ideal rows, then computing utility degrees relative to these extended-matrix anchors. The canonical Stević 2020 utility function F(U) = (U+ + U-)/(1 + (1-F(U+))/F(U+) + (1-F(U-))/F(U-)) combines both utilities. SFZN reliability (τ_ε, τ_ν, τ_∂) modulates each component via the (component × reliability) Euclidean distance in Step 6, then co
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
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Sources
- 1.Niu, J. (2024). Spherical Fuzzy Z-Numbers-based CRITIC CRADIAS and MARCOS Approaches for Evaluating English Teacher Performance. International Journal of Advanced Computer Science and Applications (IJACSA)
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Cite this page
ScholarGate. (2026, June 2). SFZN-MARCOS. ScholarGate. https://scholargate.app/decision-making/sfzn-marcos