IV-WASPAS - WASPAS yönteminin Aralık Değerli Sezgisel Bulanık uzantısı

Formül adımları

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

1. normalization

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

   \tilde{a}_{ij}^c=([\nu^L_{ij},\nu^U_{ij}],[\mu^L_{ij},\mu^U_{ij}])\quad\text{for cost criteria}

2. ranking_computation

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

   Q_i^{(1)}=s\Bigl(\mathrm{IIFWA}(w,\hat{a}_i)\Bigr)=s\Bigl([1-\textstyle\prod_j(1-\mu^L_{ij})^{w_j},\ldots],\;[\textstyle\prod_j\nu^L_{ij}{}^{w_j},\ldots]\Bigr)

3. ranking_computation

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

   Q_i^{(2)}=s\Bigl(\mathrm{IIFWG}(w,\hat{a}_i)\Bigr)=s\Bigl([\textstyle\prod_j\mu^L_{ij}{}^{w_j},\textstyle\prod_j\mu^U_{ij}{}^{w_j}],\;[1-\textstyle\prod_j(1-\nu^L_{ij})^{w_j},\ldots]\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: iv-waspas extends WASPAS to handle Interval uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Interval Number (IN: [a, b]) algebra. The final scores are defuzzified via midpoint (a+b)/2 before ranking.

Varsayımlar

• Decision matrix entries are valid Interval 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 IN: a ≤ b koşulunu sağladığından emin olun.
• Defuzzifikasyon yöntemi sıralamayı etkiler: midpoint (a+b)/2 kanonik seçimdir.