The Comparison of Neural Networks’ Structures for Forecasting
This paper considers the application of neural networks to demand forecasting in a simple supply chain composed of a single retailer and his supplier with a game theoretic approach. This work analyses the problem from the supplier’s point of view and the employed dataset in our experimentation is pr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Kharazmi University
2017-05-01
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| Series: | International Journal of Supply and Operations Management |
| Subjects: | |
| Online Access: | http://www.ijsom.com/article_2729_a658b3f1087d490d523d43b0d828838b.pdf |
| Summary: | This paper considers the application of neural networks to demand forecasting in a simple supply chain composed of a single retailer and his supplier with a game theoretic approach. This work analyses the problem from the supplier’s point of view and the employed dataset in our experimentation is provided from a recognized supermarket in Morocco. Various attempts were made in order to optimize the total network error and the findings indicate that different neural net structures can be used to forecast demand such as Adaline, Multi-Layer Perceptron (MLP), or Radial Basis Function (RBF) Network. However, the most adequate one with optimal error is the MLP architecture. |
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| ISSN: | 2383-1359 2383-2525 |