Journal
APPLIED MATHEMATICAL MODELLING
Volume 36, Issue 10, Pages 4714-4727Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2011.12.006
Keywords
Artificial intelligence (AI); Neural networks; Locally linear neuro-fuzzy model; Supply chain management; Supplier selection problem
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Supplier selection and evaluation is a complicated and disputed issue in supply chain network management, by virtue of the variety of intellectual property of the suppliers, the several variables involved in supply demand relationship, the complex interactions and the inadequate information of suppliers. The recent literature confirms that neural networks achieve better performance than conventional methods in this area. Hence, in this paper, an effective artificial intelligence (AI) approach is presented to improve the decision making for a supply chain which is successfully utilized for long-term prediction of the performance data in cosmetics industry. A computationally efficient model known as locally linear neuro-fuzzy (LLNF) is introduced to predict the performance rating of suppliers. The proposed model is trained by a locally linear model tree (LOLIMOT) learning algorithm. To demonstrate the performance of the proposed model, three intelligent techniques, multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network and least square-support vector machine (LS-SVM) are considered. Their results are compared by using an available dataset in cosmetics industry. The computational results show that the presented model performs better than three foregoing techniques. (C) 2011 Elsevier Inc. All rights reserved.
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