Abstract

Son yıllarda, geri yay ılım tekniğine dayanan yapay sinir ağı Ziarati and Ucan, January 2001 modeli ile gerçek bir firmanın malzeme tedarik zincirinde geleceğe dönük malzeme talep miktarı tahmin edilebilmiştir. Yapay sinir ağlarının hızlı olması, büyük miktardaki verinin ele alınabilmesi, malzeme akış diagramlarında geleceğe yönelik tahminlerde potensiel bir model olmalarını sağlamaktadır. Bu makale, Ziarati and Ucan, January 2001 makalesinin geliştirilmiş biçimidir. Burada yapay sinir ağ YSA yapısı yerine Genetik Hücresel Yapay Sinir Ağ HYSA modeli konulmuştur. Söz konusu yaklaşım daha az parametre ile kestirim yapabilmekte ve dolayısıyla hızlı değişimli gerçek tedarik zincir problemlerine daha hızlı uyum sağlamaktadır. Önerilen modelin, tedarik zinciri problemlerinde, gerek eğitim sürecinin kısaltılmasında gerekse malzeme istek kestirimde üstün başarım göstermesi beklenmektedir.

Keywords

• Genetik Hücresel Yapay Sinir Ağ Yapıları, Tedarik Zincirleri

DESIGN AND DEVELOPMENT OF MATERIAL AND INFORMATION FLOW FOR SUPPLY CHAINS USING GENETIC CELLULAR NETWORKS

Abstract

In a recent paper by authors Ziarati and Ucan, January 2001 a Back Propagation-Artificial Neural Network BP-ANN was adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. It was argued that due to the learning ability of neural networks, their speed and capacity to handle large amount of data, they have a potential for predicting components requirements and establishing associated scheduling throughout a given supply chain system. This paper should be considered a continuation of the first paper as the neural network approach introduced in this paper replaces the BP-ANN by a new method viz., Genetic Cellular Neural Network GCNN . The latter approach requires by far less stability parameters and hence better suited to fast changing scenarios as in real supply chain applications. The model has shown promising outcomes in learning and predicting material demand in a supply chain, with high degree of accuracy.

Keywords

• Genetic Cellular Neural Network, Supply Chain

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