Comparison of artificial neural networks learning methods to evaluate supply chain performance
نویسندگان
چکیده
Abstract: The supply chain performance evaluation is a critical activity to continuously improve operations. Literature presents several systems based on multi-criteria methods and artificial intelligence. Among them, the neural networks (ANN) excel due their capacity of modeling non-linear relationships between metrics allowing adaptations specific environment by means historical data. These systems’ accuracy depend directly adopted training algorithm, no studies have been found that assess efficiency these algorithms when applied evaluation. In this context, present study evaluates four ANNs learning in order investigate which one most adequate deal with tested were Gradient Descendent Momentum, Levenberg-Marquardt, Quasi-Newton Scale Conjugate Gradient. extracted from SCOR®, reference model used worldwide. random sub-sampling cross-validation method was find topological configuration for each model. A set 80 topologies implemented using MATLAB®. prediction mean square error. For level 1 considered, Levenberg-Marquardt algorithm provided precise results. results correlation analysis hypothesis tests reinforce proposed models. Furthermore, computational models reached higher than previous approaches.
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ژورنال
عنوان ژورنال: Gestão & produção
سال: 2021
ISSN: ['1806-9649', '0104-530X']
DOI: https://doi.org/10.1590/1806-9649-2021v28e5450