TOPSIS with Maximizing Deviation in Simplified Neutrosophic Hesitant Fuzzy Environment
SNHF-TOPSIS (TOPSIS with Maximizing Deviation in Simplified Neutrosophic Hesitant Fuzzy Environment) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M. Naz, S. Smarandache, F. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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When to use it
SNHF-TOPSIS handles decision problems where experts assign multiple possible truth/indeterminacy/falsity values per evaluation (hesitant neutrosophic data). Criterion weights are derived automatically from data dispersion via Maximizing Deviation — no external weight input needed. The RC score measures relative closeness to the ideal solution: RC closer to 1 means better alternative.
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.
Sources
- Akram, M., Naz, S., Smarandache, F. (2019). Generalization of Maximizing Deviation and TOPSIS Method for MADM in Simplified Neutrosophic Hesitant Fuzzy Environment. Symmetry DOI: 10.3390/sym11081058 ↗
How to cite this page
ScholarGate. (2026, June 2). TOPSIS with Maximizing Deviation in Simplified Neutrosophic Hesitant Fuzzy Environment. ScholarGate. https://scholargate.app/en/decision-making/snhf-topsis
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