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Design and Development of Material and Information Flow for Supply Chaıns Using Genetic Cellular Networks = Genetik Hücresel Yapay Sinir Ağ Yapıları Kullanılarak Tedarik Zincirleri için Bilgi ve Malzeme Akışının Geliştirilmesi

Author(s): Martin ZIARATI | David STOCKTON | Osman N. UÇAN | Erdem BİLGİLİ

Journal: Dogus University Journal
ISSN 1302-6739

Issue: 5;
Start page: 193;
Date: 2002;
Original page

Keywords: Genetic Cellular Neural Network | Supply Chain

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.

Tango Jona
Tangokurs Rapperswil-Jona

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