N-RAWEC - Neutrosophic extension of RAWEC

Overview

n-rawec extends RAWEC to handle Neutrosophic uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3) algebra. The final scores are defuzzified via score function S = (T − F + 1)/2 before ranking.

Output

utility, higher is better

Data

Triangular Neutrosophic, uncertainty tuples complete

Weights

Needs a weight source

Size

2+ alternatives, 3-10 criteria works best

Used for

Single-Valued Neutrosophic MCDM, MAGDM under epistemic uncertainty, expert-driven evaluation with linguistic terms

How it works

1. 1

   NS karar matrisini oluştur, score fonksiyonu ile crispify: s_ij = (1+T-2I-F)/2.

2. 2

   İki yönlü normalizasyon. Fayda: n_ij = s_ij/max_i(s_ij). Non-fayda: n*_ij = min_i(s_ij)/s_ij.

3. 3

   Sapma toplamları. v_i = Σ w_j(1−n_ij), v*_i = Σ w_j(1−n*_ij).

4. 4

   RAWEC indeksi. Q_i = (v*_i − v_i)/(v*_i + v_i). Aralık [−1,1].

5. 5

   Q_i'ye göre azalan sıralama. Paper sonucu: A1=0.188 > A2=−0.024 > A4=−0.082 > A3=−0.193.

Fits when / Look elsewhere when

Assumptions to verify

• Decision matrix entries are valid Single-Valued Neutrosophic numbers/tuples
• Underlying crisp method's compensation assumption holds in uncertain space
• All decision-maker(s) and experts use the same linguistic/uncertainty scale

Edge cases and pitfalls

• Value-space violation: ensure all entries satisfy SVNS: T,I,F ∈ [0,1]; 0 ≤ T+I+F ≤ 3 before computation.

• Defuzzification method affects ranking: score function S = (T − F + 1)/2 is the canonical choice but alternatives exist.

Works with

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