Belirsiz Kriter Ağırlıkları Altında Kurumsal Masaüstü Bilgisayar Tedariki: SMAA-2 Uygulaması
DOI:
https://doi.org/10.63556/tisej.2026.1923Anahtar Kelimeler:
Çok Kriterli Karar Verme- SMAA- SMAA-2- Kurumsal Bilgisayar Alımı- Monte Carlo SimülasyonuÖz
Kurumların bilgi işlem altyapılarının yenilenmesi, maliyet, performans, teknik yeterlilik ve servis erişimi gibi birbiriyle çelişebilen çok sayıda kriterin eş zamanlı değerlendirilmesini gerektirmektedir. Farklı paydaşların öncelik yapılarındaki belirsizlik, deterministik ağırlıklara dayalı klasik çok kriterli karar verme (ÇKKV) yöntemlerinin bu tür kararlarda sınırlı kalmasına yol açabilmektedir.
Bu çalışmada, kurumsal masaüstü bilgisayar tedarikinde kriter ağırlıklarının belirsiz olduğu bir karar ortamı için Stokastik Çok Kriterli Kabul Edilebilirlik Analizi (SMAA-2) yöntemi uygulanmıştır. Çalışmada alternatiflere ait kriter değerleri deterministik olarak ele alınmış; tercih belirsizliği ise kriter ağırlıklarının ağırlık simpleksi üzerinde düzgün dağılım varsayımıyla örneklenmesi yoluyla modellenmiştir. Türkiye pazarında erişilebilir 18 masaüstü bilgisayar modeli; işlemci performansı, bellek ve depolama özellikleri, servis erişimi ve fiyatı temsil eden ana kriter boyutları ile bunlara ilişkin teknik göstergeler çerçevesinde değerlendirilmiştir. JSMAA yazılımında 10.000 Monte Carlo iterasyonu gerçekleştirilmiş; Sıralama Kabul Edilebilirlik Endeksi (RAI), Merkezi Ağırlık Vektörü (CWV) ve Güven Faktörü (CF) çıktıları elde edilmiştir.
Analiz sonuçlarına göre Pro Tower 290 G9 modeli, mevcut kriter yapısı altında %55,5 oranında birinci sırada yer alma olasılığı ve 0,71'lik güven faktörü ile öne çıkmaktadır. CWV çıktıları, farklı alternatiflerin hangi tercih yapılarında öne çıktığını; CF analizi ise bu tercihlerin göreli istikrar düzeyini ortaya koymuştur. Sonuç olarak çalışma, deterministik tekil sıralamalar yerine tercih belirsizliğini doğrudan dikkate alan olasılıksal bir değerlendirme çerçevesi sunmakta ve kurumsal donanım yatırımları için veriye dayalı, esnek ve yorumlanabilir bir karar destek yaklaşımı önermektedir.
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