SNHF-TOPSIS - TOPSIS with Maximizing Deviation in Simplified Neutrosophic Hesitant Fuzzy Environment

Overview

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.

Output

relative closeness, higher is better

Data

Hesitant, svnhfe sets complete

Weights

Derived internally, no weight source needed

Size

2+ alternatives, 3-8 criteria works best

Used for

Supplier selection under expert hesitancy, Product evaluation with multiple domain experts, MAGDM with linguistic neutrosophic uncertainty

Look elsewhere when

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)

Edge cases and pitfalls

• Length mismatch: always apply Algorithm 1 (F2) before distance computation - unequal SVNHFE component lengths make distances incomparable.

• Do not supply fixed external weights: if weights are precisely known, use N-TOPSIS with supplied weights instead.

• Sensitivity to alpha: RC scores change with alpha. Paper Figure 3 shows alpha=1 through 10. Run sensitivity analysis.

Works with

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