Abstract
In sustainable supply chain networks, companies are obligated to have a systematic decision support system in place to help it adopt right decisions at right times. Among strategic decisions, supplier selection and evaluation outranks other decisions in terms of importance due to its long-term impacts. Besides, the adoption of such strategic decision entails exploring several factors that contribute to the complexity of decision making in the supply chain. For the purpose of solving non-linear regression problems, a novel neural network technique known as least square-support vector machine (LS-SVM) with maximum generalization ability has successfully been implemented. However, the performance quality of the LS-SVM is recognized to notoriously vary depending on the rigorous selection of its parameters. Therefore, in this paper, a continuous general variable neighborhood search (CGVNS) which is an effective meta-heuristic algorithm to solve the real world engineering continuous optimization problems is proposed to be integrated with LS-SVM. The CGVNS is hybridized in our novel integrated LS-SVM and CGVNS model, to tune the parameters of the LS-SVM to better estimate performance rating of supplier selection and evaluation problem. To demonstrate the improved performance of our proposed integrated model, a real data set from a case study of a supplier selection and evaluation problem is presented in a cosmetics industry. Additionally, comparative evaluations between our proposed model and the conventional techniques, namely nonlinear regression, multi-layer perceptron (MLP) neural network and LS-SVM is provided. The experimental results simply manifest the outperformance of our proposed model in terms of estimation accuracy and effective prediction.
Highlights
Pressed with today’s global marketplace characterized by globalization, flourishing customers’ expectations, expanding regulatory conformity, global economic recession, and fierce competitive pressure, manufacturers cannot take on a life of their own
The results demonstrate the capability of the proposed model to produce lower error rates as compared to the three intelligent techniques for the selection problem in the supply chain management
The encouraging results obtained by the proposed least squaresupport vector machine (LS-support vector machine (SVM))-Continuous general variable neighborhood search (CGVNS) model indicate a positive opportunity to be considered and to be utilized in the future for the supply chain decisions for the long-term, mid-term and short-term planning by introducing suitable prediction outcomes from the context of this paper
Summary
Pressed with today’s global marketplace characterized by globalization, flourishing customers’ expectations, expanding regulatory conformity, global economic recession, and fierce competitive pressure, manufacturers cannot take on a life of their own. AI-based models are recognized to be the best methods for selecting and evaluating the suppliers in the supply chain. SVMs, due to their excellent performance in generalization and their capacity for self-learning, have overcome the potential weaknesses of conventional prediction techniques, namely artificial neural networks (ANNs) and fuzzy systems in realworld applications 9. In this paper an attempt is made to streamline the performance rating of supplier in supplier selection and evaluation problem by introducing a novel hybrid meta-heuristic support vector model. The proposed model is validated by using a real data set gathered from a case study for supplier selection and evaluation problem in a cosmetics industry.
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