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PIF-WASPAS

PiF-WASPAS - WASPAS yönteminin Picture Bulanık uzantısı

Picture WSM-WPM hibrit sıralama - Resim Bulanık Sayı (PiFN: μ, η, ν; μ+η+ν ≤ 1)

Formül adımları

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

  1. Adım 1 — Picture bulanık karar matrisini X̃ = [x̃_ij]_{m×n} oluştur; her hücre x̃_ij = ⟨μ_ij, η_ij, ν_ij⟩ PiFN koşulunu sağlar; reddetme π_ij = 1−μ_ij−η_ij−ν_ij. Kriter yönü PiFN kodlamasından önce dilbilimsel dönüşümde işlenir.

    x̃ij=⟨μij,ηij,νij⟩;μij,ηij,νij∈[0,1];μij+ηij+νij≤1;πij=1−μij−ηij−νij(Cuong2013PiFSDef1;Chowdhury2025§3.2Eq.(1)−(2))
    LaTeX x̃_ij = ⟨μ_ij, η_ij, ν_ij⟩; μ_ij, η_ij, ν_ij ∈ [0,1]; μ_ij+η_ij+ν_ij ≤ 1; π_ij = 1−μ_ij−η_ij−ν_ij (Cuong 2013 PiFS Def 1; Chowdhury 2025 §3.2 Eq.(1)-(2))
  2. Adım 2 — Picture bulanık kriter ağırlıkları v̄_j dışsal verilir (DM toplanır, Chowdhury 2025 §4.1 Tablo 4-5). Crisp ağırlık Eq.(16) ile. PiF-WASPAS HER İKİSİNİ kullanır: WSM (Eq.17) içinde v̄_j (PiFN), WPM (Eq.18) içinde PiFN-üs üsleri olarak w_j (crisp).

    v̄j=⟨μj,ηj,νj⟩,μj+ηj+νj≤1.Crisp:wj=[μj+ηj/2+(πj/2)(1+μj−νj)]/Σk[μk+ηk/2+(πk/2)(1+μk−νk)](Chowdhury2025Eq.(15)−(16))
    LaTeX v̄_j = ⟨μ_j, η_j, ν_j⟩, μ_j+η_j+ν_j ≤ 1. Crisp: w_j = [μ_j + η_j/2 + (π_j/2)(1+μ_j-ν_j)] / Σ_k [μ_k + η_k/2 + (π_k/2)(1+μ_k-ν_k)] (Chowdhury 2025 Eq.(15)-(16))
  3. Adım 3 — Eq.(17): Q^(1)_i = Σ⊕_{j=1}^n (v̄_j ⊗ x̃_ij). Her ağırlıklı hücre v̄_j ⊗ x̃_ij Cuong 2013 Eq.(4) PiFN çarpımı; kriterler arası agregasyon Cuong 2013 Eq.(3) PiFN toplamı. Doğrulama: Chowdhury 2025 Tablo 9 WSM kolonu TAM TAM yeniden üretildi (16/16).

    LaTeX Q^(1)_i = Σ⊕_{j=1}^n (v̄_j ⊗ x̃_ij) = ⟨1 − Π_j (1 − μ_v_j·μ_x_ij), Π_j (η_v_j+η_x_ij−η_v_j·η_x_ij), Π_j ((η_v_j+η_x_ij−η_v_j·η_x_ij)+(ν_v_j+ν_x_ij−ν_v_j·ν_x_ij)) − Π_j (η_v_j+η_x_ij−η_v_j·η_x_ij)⟩ (Chowdhury 2025 §3.3 Eq.(17); Cuong 2013 Eq.(3)-(4))
  4. Adım 4 — Eq.(18): Q^(2)_i = Π⊗_j (x̃_ij)^{w_j}. Her hücre PiFN-üs Cuong 2013 Eq.(6); kriter aralığı Cuong 2013 Eq.(4) PiFN çarpımı. KRİTİK NOT: Chowdhury 2025 Tablo 9 WPM kolonu hesap hatası içerir — T1 = ⟨0.690,0.174,0.170⟩ sum=1.034>1 (PiFN kısıtını ihlal). Kanonik Eq.(18) T1 = ⟨0.348,0.174,0.170⟩ (sum=0.692, geçerli PiFN). Doğrulama: η ve ν tüm 16 satır için TAM eşleşir; μ farklılaşır çünkü paper'ın μ değerleri yanlış hesaplanmış. Manifest kanoniği uygular.

    LaTeX Q^(2)_i = Π⊗_{j=1}^n (x̃_ij)^{w_j} where (x̃_ij)^{w_j} = ⟨μ_x_ij^{w_j}, 1−(1−η_x_ij)^{w_j}, 1−(1−ν_x_ij)^{w_j}⟩ (Eq.(6)); aggregation = Π via PiFN ⊗ Eq.(4): μ_Q2 = Π_j μ_x_ij^{w_j}, η_Q2 = combined via η ⊗ recursion, ν_Q2 likewise (Chowdhury 2025 §3.3 Eq.(18); Cuong 2013 Eq.(4),(6))
  5. Adım 5 — Eq.(19) WASPAS PiFN birleşik skoru Q̃_i = 0.5·Q^(1)_i ⊕ 0.5·Q^(2)_i. Uygulama: Cuong 2013 Eq.(5) skaler çarpım (λA, λ=0.5) ile her bileşeni yarımla, sonra Eq.(3) ⊕ PiFN toplamı. λ=0.5 Chowdhury 2025 seçimi (Zavadskas 2012 crisp WASPAS varsayılanı ile uyumlu).

    Q̃i=0.5·Q(1)i⊕0.5·Q(2)i;0.5·A=⟨1−(1−μA)0.5,ηA0.5,νA0.5⟩(Eq.(5));⊕perEq.(3)(Chowdhury2025§3.3Eq.(19))
    LaTeX Q̃_i = 0.5·Q^(1)_i ⊕ 0.5·Q^(2)_i; 0.5·A = ⟨1−(1−μ_A)^{0.5}, η_A^{0.5}, ν_A^{0.5}⟩ (Eq.(5)); ⊕ per Eq.(3) (Chowdhury 2025 §3.3 Eq.(19))
  6. Adım 6 — Her birleşik Q̃_i'yi crisp F_i'ye defuzzifiye et Eq.(20) ile: F_i = (μ_{Q̃_i} + (1 − η_{Q̃_i}) + (1 − ν_{Q̃_i})) / 3. F_i ∈ [0, 1]; büyük daha iyi. Alternatifleri F_i'ye göre azalan sırala.

    Fi=(μQ̃i+(1−ηQ̃i)+(1−νQ̃i))/3;ranking=argsortdesc(F);best=ranking[0](Chowdhury2025§3.3Eq.(20))
    LaTeX F_i = (μ_{Q̃_i} + (1 − η_{Q̃_i}) + (1 − ν_{Q̃_i})) / 3; ranking = argsort_desc(F); best = ranking[0] (Chowdhury 2025 §3.3 Eq.(20))

Yöntem ayrıntıları kaynak kütüphanedeki özgün (İngilizce) metindir.

Sezgi

Picture WSM-WPM hybrid ranking - Picture Fuzzy Number (PiFN: μ, η, ν; μ+η+ν ≤ 1). Output typically utility (higher value = preferred).

Sonucu okuma: pif-waspas extends Zavadskas et al. 2012 crisp WASPAS to Picture fuzzy uncertainty via Cuong 2013 PiFS. Computes BOTH a WSM component (Q^(1) via PiFN-weighted sum of cells) AND a WPM component (Q^(2) via PiFN-power product of cells using crisp criteria weights as exponents), then combines them via PiFN convex combination Q̃ = 0.5·Q^(1) ⊕ 0.5·Q^(2). Final score F_i = (μ + (1-η) + (1-ν))/3 ∈ [0,1]. Higher F_i is better. The dual WSM+WPM design provides compromise between full-compensation (sum) and limited-compensation (product) aggregation styles.

Varsayımlar

  • Decision matrix entries are valid PiFNs (μ+η+ν ≤ 1)
  • Both PiFN weights v̄_j and crisp w_j provided/derivable
  • Criterion-direction inversion handled upstream OR via PiFN complement
  • Same linguistic/PiFN scale across all decision-maker(s)

Ne zaman kullanılmaz

  • Single-aggregation behaviour wanted (use PiF-SAW for sum-only or PiF-ARAS for ratio)
  • Data are crisp - base WASPAS suffices
  • Neutral-stance modelling unnecessary - IF-WASPAS or fuzzy-WASPAS lighter alternative

Sınırlılıklar

  • Rank reversal known on alternative-set changes (ref: inherited from crisp WASPAS base; cf. Belton-Gear 1983, Wang-Luo 2009; Zavadskas et al. 2012)
  • Assumes: Decision matrix entries are valid PiFNs (μ+η+ν ≤ 1)
  • Assumes: Both PiFN weights v̄_j and crisp w_j provided/derivable
  • Assumes: Criterion-direction inversion handled upstream OR via PiFN complement
  • Assumes: Same linguistic/PiFN scale across all decision-maker(s)

Sık yapılan hatalar

  • Değer-uzayı ihlali: hesaplamadan önce tüm girişlerin PiFN μ+η+ν ≤ 1 koşulunu sağladığından emin olun. PFN (Pythagorean) ve IFS girdileri geçerli PiFN DEĞİLDİR.
  • Çift ağırlıklandırma: PiF-WASPAS HEM PiFN ağırlıkları v̄_j (WSM Eq.(17) içinde ⊗ operandı) HEM crisp ağırlıklar w_j (WPM Eq.(18) içinde PiFN-üs üssü) kullanır. Yalnız birini sağlamak başarısız olur; diğerini Eq.(16) defuzzifikasyon ile türetin.
  • Yön işleme: kriter yönü dilbilimsel dönüşümde kodlanır. criteria_types='min' ile ham PiFN için cost sütunlarına F3'ten önce PiFN komplemanı uygulanmalı.
  • Paper Tablo 9 WPM μ kolonu güvenilmez: Chowdhury 2025 Tablo 9 WPM μ değerleri yanlış hesaplanmış (T1 ⟨0.690,0.174,0.170⟩ sum=1.034>1, PiFN ihlali). η ve ν kolonları doğru. Manifest kanonik Eq.(18) (Cuong 2013 Eq.(6)+Eq.(4)) uygular; ilk/son alternatif paper ile eşleşir, orta pozisyonlar ayrışır.

Hesap adımları ve dayanakları

  1. Construct the Picture fuzzy decision matrix X̃ = [x̃_ij]_{m×n} where each entry x̃_ij = ⟨μ_ij, η_ij, ν_ij⟩ is a PiFN satisfying μ_ij+η_ij+ν_ij ≤ 1, with refusal π_ij = 1−μ_ij−η_ij−ν_ij. Criterion-direction is handled at the linguistic-conversion step (Chowdhury 2025 §4.1 Table 6: cost criteria mapped so lower raw values receive higher μ); the resulting PiFN matrix is therefore treated as all-benefit at the algorithm-internal level.

    Dayanak: Chowdhury 2025 §3.2 Eq.(1)-(2); Cuong 2013

  2. Picture fuzzy criterion weights v̄_j = ⟨μ_j, η_j, ν_j⟩ are supplied externally (DM-elicited, aggregated per Chowdhury 2025 §4.1 Tables 4-5) or derived from a Picture fuzzy weighting method. The corresponding crisp weight via Eq.(16): w_j = [μ_j + η_j/2 + (π_j/2)(1+μ_j-ν_j)] / Σ_k [...]. PiF-WASPAS uses BOTH: v̄_j (PiFN) in Q^(1) WSM (Eq.17), and w_j (crisp) in Q^(2) WPM (Eq.18) as PiFN-power exponents.

    Dayanak: Chowdhury 2025 §3.3 Eq.(15)-(16)

  3. Compute Q^(1) WSM component per Eq.(17): Q^(1)_i = Σ⊕_{j=1}^n (v̄_j ⊗ x̃_ij). Each weighted cell v̄_j ⊗ x̃_ij is a PiFN computed via Cuong 2013 Eq.(4) PiFN product; cells are aggregated across criteria via Cuong 2013 Eq.(3) PiFN sum. NOTE: The Σ⊕ aggregation here is the same one as in PiF-ARAS Eq.(30): μ_Q1 = 1−Π(1−μ), η_Q1 = Π η, ν_Q1 = Π(η+ν)−Π η. Reproduction-verified: Chowdhury 2025 Table 9 WSM column reproduces EXACTLY (16/16) under this implementation.

    Dayanak: Chowdhury 2025 §3.3 Eq.(17)

  4. Compute Q^(2) WPM component per Eq.(18): Q^(2)_i = Π⊗_{j=1}^n (x̃_ij)^{w_j}. Each per-cell PiFN-power (x̃_ij)^{w_j} via Cuong 2013 Eq.(6): A^λ = ⟨μ^λ, 1−(1−η)^λ, 1−(1−ν)^λ⟩; powered cells aggregated across criteria via Cuong 2013 Eq.(4) PiFN product. CRITICAL NOTE: Chowdhury 2025 Table 9 WPM column has computational errors - reported T1 = ⟨0.690, 0.174, 0.170⟩ has sum=1.034>1 (violates PiFN constraint). Canonical Eq.(18) gives T1 = ⟨0.348, 0.174, 0.170⟩ (sum=0.692, valid PiFN). Verified: η AND ν columns of canonical match Table 9 EXACTLY for all 16 rows; μ column differs because paper's μ values were apparently miscomputed (then propagated into Table 10 F_i and ranking). Manifest implements canonical Eq.(18) per the published operational laws.

    Dayanak: Chowdhury 2025 §3.3 Eq.(18)

  5. Combined WASPAS PiFN score Q̃_i per Eq.(19): Q̃_i = 0.5·Q^(1)_i ⊕ 0.5·Q^(2)_i. Implementation uses Cuong 2013 Eq.(5) scalar multiplication (λA = ⟨1−(1−μ)^λ, η^λ, ν^λ⟩) with λ=0.5 to halve each component, then PiFN sum Eq.(3) ⊕ to combine. The convex combination weight λ=0.5 is the Chowdhury 2025 choice (matches default in Zavadskas 2012 crisp WASPAS).

    Dayanak: Chowdhury 2025 §3.3 Eq.(19)

  6. Defuzzify each combined Q̃_i to crisp F_i via Eq.(20): F_i = (μ_{Q̃_i} + (1 − η_{Q̃_i}) + (1 − ν_{Q̃_i})) / 3. F_i ∈ [0, 1]; higher is better. Rank alternatives in descending order of F_i.

    Dayanak: Chowdhury 2025 §3.3 Eq.(20)