IF-WASPAS - WASPAS yönteminin Intuitionistic uzantısı

Formül adımları

Analiz motorunun yöntem bildirimindeki (manifest F.steps) adımlar; raporlardaki formüllerle aynı kaynaktır.

1. normalization

   $${\tilde{a}}_{ij}^c=({\nu }_{ij},{\mu }_{ij})\text{for cost criteria}$$
   LaTeX

   \tilde{a}_{ij}^c=(\nu_{ij},\mu_{ij})\quad\text{for cost criteria}

2. ranking_computation

   $$Q_i^{(1)}=s(\mathrm{I}\mathrm{F}\mathrm{W}\mathrm{A}(w,{\tilde{a}}_i))=s(1-\prod _j(1-{\mu }_{ij}{)}^{w_j},\prod _j{\nu }_{ij}^{w_j})$$
   LaTeX

   Q_i^{(1)}=s\Bigl(\mathrm{IFWA}(w,\tilde{a}_i)\Bigr)=s\Bigl(1-\prod_j(1-\mu_{ij})^{w_j},\;\prod_j\nu_{ij}^{w_j}\Bigr)

3. ranking_computation

   $$Q_i^{(2)}=s(\mathrm{I}\mathrm{F}\mathrm{W}\mathrm{G}(w,{\tilde{a}}_i))=s(\prod _j{\mu }_{ij}^{w_j},1-\prod _j(1-{\nu }_{ij}{)}^{w_j})$$
   LaTeX

   Q_i^{(2)}=s\Bigl(\mathrm{IFWG}(w,\tilde{a}_i)\Bigr)=s\Bigl(\prod_j\mu_{ij}^{w_j},\;1-\prod_j(1-\nu_{ij})^{w_j}\Bigr)

4. ranking_computation

   $$Q_i=\lambda Q_i^{(1)}+(1-\lambda )Q_i^{(2)},\lambda =0.5,\text{rank descending}$$
   LaTeX

   Q_i=\lambda Q_i^{(1)}+(1-\lambda)Q_i^{(2)},\quad\lambda=0.5,\quad\text{rank descending}

Sezgi

Sonucu okuma: if-waspas extends WASPAS to handle Intuitionistic uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1) algebra. The final scores are defuzzified via score function S = μ − ν before ranking.

Varsayımlar

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

Ne zaman kullanılmaz

• Classical data sufficient - use base WASPAS directly (avoid unnecessary uncertainty layer)
• Aggregation operator (PFWA/PFOWA/etc.) not specified - output ambiguous

Sık yapılan hatalar

• Değer-uzayı ihlali: hesaplamadan önce tüm girişlerin IFN: μ ∈ [0,1], ν ∈ [0,1], μ+ν ≤ 1; π = 1−μ−ν ≥ 0 koşulunu sağladığından emin olun.
• Defuzzifikasyon yöntemi sıralamayı etkiler: score function S = μ − ν kanonik seçimdir.