A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study

DOI

10.2991/ijcis.2017.10.1.20How to use a DOI?

Keywords

Computational intelligence; Least square-support vector machine (LS-SVM); Supplier selection; Supplier Evaluation; Continuous general variable neighborhood search (CGVNS); Cosmetics industry

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Behnam Vahdani
AU  - S. Meysam Mousavi
AU  - R. Tavakkoli-Moghaddam
AU  - H. Hashemi
PY  - 2017
DA  - 2017/01/01
TI  - A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study
JO  - International Journal of Computational Intelligence Systems
SP  - 293
EP  - 311
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.20
DO  - 10.2991/ijcis.2017.10.1.20
ID  - Vahdani2017
ER  -