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Supplier Evaluation and Selection Using Restricting Weights Method in Fuzzy Environment

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Proceedings of the Eighth International Conference on Management Science and Engineering Management

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 280))

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Abstract

Data envelopment analysis (DEA) is a method for supplier selection. Weight restrictions allow for the integration of managerial preferences in terms of relative importance levels of various inputs and outputs. In some situations there are some factors which play both input and output roles as well. The purpose of this research is to propose a method for selecting the best suppliers in the presence of weight restrictions and dual-role factors. This study shows the supplier selection process through a DEA model, while allowing for the incorporation of decision makers preferences and considers multiple factors which simultaneously play both input and output roles. The proposed model does not demand exact weights from the decision maker. This study presents a robust model to solve the multiple-criteria problem. A numerical example certifies the application of the proposed method.

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Acknowledgments

The authors wish to thank the anonymous referees for their helpful and constructive comments and suggestions. The work is supported by the National Natural Science Foundation of China (Grant No. 71301109), the Western and Frontier Region Project of Humanity and Social Sciences Research, Ministry of Education of China (Grant No. 13XJC630018), the Philosophy and Social Sciences Planning Project of Sichuan province (Grant No. SC12BJ05), and the Initial Funding for Young Teachers of Sichuan University (Grant No. 2013SCU11014).

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Correspondence to Abid Hussain Nadeem .

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Nadeem, A.H., Nazim, M., Hashim, M., Javed, M.K., Yao, L. (2014). Supplier Evaluation and Selection Using Restricting Weights Method in Fuzzy Environment. In: Xu, J., Cruz-Machado, V., Lev, B., Nickel, S. (eds) Proceedings of the Eighth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing, vol 280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55182-6_62

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  • DOI: https://doi.org/10.1007/978-3-642-55182-6_62

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-55181-9

  • Online ISBN: 978-3-642-55182-6

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