İmalat hücresi oluşturulması için farklı kümeleme yöntemlerinin performans karşılaştırması

Bu çalışma, hücresel imalat sistemi tasarımının temel ve önemli aşaması olan hücre oluşturmaya değinmektedir. Çalışmada hücre oluşturma uygulamalarında yaygın olarak kullanılan üç yöntem; kortalamalar kümeleme algoritması, ortalama bağlantılı kümeleme algoritması ve beklenti maksimizasyonu algoritmasını kullanan bulanık kümeleme algoritması incelenmektedir. Bir inşaat ekipmanı üreticisinin silindir bölümünün tasarımı için bu yöntemlerin gerçek hayat uygulaması gerçekleştirilmiştir. Uygulanan her algoritmanın performansı hücre içi boşluklar, hücre içi işlem yoğunluğu ve hücreler arası taşıma miktarı ölçütlerine göre değerlendirilmektedir. Uygulama sonuçları, klasik kümeleme algoritmalarından en çok bilinen ve en yaygın olarak uygulanan k-ortalamalar kümeleme algoritmasının hücre oluşturma için hala etkili bir yöntem olduğunu göstermektedir.

Performance comparison of different clustering methods for manufacturing cell formation

This study refers to cell formation, which is the fundamental and important stage of cellular manufacturingsystem design. Three widely used methods that are K-means clustering algorithm, average-linkageclustering algorithm and fuzzy clustering using expectation maximization algorithm for cell formationproblem are studied. A real life application of these methods for the design of cylinder department of aconstruction equipment manufacturer is performed. The performance of each applied algorithm is evaluatedaccording to intracellular voids, intracellular processing intensity and intercellular transportation measures.The application results indicate that K-means clustering algorithm, which is the most widely applied andmost known one of classical clustering algorithms, is still an effective method for cell formation.

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