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.
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
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.
Common pitfalls
This section is available to Pro members. Upgrade to Pro
Sources
- 1.Akram, M., Naz, S., Smarandache, F. (2019). Generalization of Maximizing Deviation and TOPSIS Method for MADM in Simplified Neutrosophic Hesitant Fuzzy Environment. Symmetry
You have read it. What now?
Cite this page
ScholarGate. (2026, June 2). SNHF-TOPSIS. ScholarGate. https://scholargate.app/decision-making/snhf-topsis