Ranking
SNHF-TOPSIS: TOPSIS with Maximizing Deviation in Simplified Neutrosophic Hesitant Fuzzy Environment
Akram, M., Naz, S., Smarandache, F. · 2019
Method assistant
Grounded explanations: it explains the method, it does not compute.
Assumptions to verify
- •Each matrix entry is a SVNHFE with all component values in [0,1]
- •Expert hesitancy sets faithfully capture evaluation uncertainty
- •lambda parameter is consistent across all entries (same expert risk posture)
When not to use
- •Single crisp or single-valued neutrosophic data: use TOPSIS or N-TOPSIS
- •Criterion weights precisely known a priori: N-TOPSIS with supplied weights is more appropriate
Commonly paired with
- •internal_maximizing_deviation + SNHF-TOPSIS (native)
How to cite
Akram, M.; Naz, S.; Smarandache, F. (2019). Generalization of Maximizing Deviation and TOPSIS Method for MADM in Simplified Neutrosophic Hesitant Fuzzy Environment. Symmetry. https://doi.org/10.3390/sym11081058