Comparison on neural networks and support vector machines in suppliers' selection |
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Authors: | Hu Guosheng Zhang Guohong |
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Institution: | 1. School of Management, Guangdong Vocational Coll. Of Science and Technology, Guangzhou 510640, P. R. China;School of Computer and Information, Anqing Teacher Coll., Anqing 246011, P. R. China 2. Adult Education Coll., Guangzhou Univ., Guangzhou 510405, P. R. China |
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Abstract: | Suppliers' selection in supply chain management (SCM) has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization,and uncontrolled convergence. A relatively new machine learning technique, support vector machine (SVM), which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers' samples are very insufficient. SVMs are adaptive to deal with small samples' training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN. |
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Keywords: | supplier selection supply chain management logistics support vector machine BPNN |
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