A novel supplier selection method that integrates the intuitionistic fuzzy weighted averaging method and a soft set with imprecise data

S.I.:Advances in Theoretical and Applied Combinatorial Optimization
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Abstract

Along with advances in technology and the advent of the information age, supply chain competition will become the core strategy of enterprises that are in pursuit of a competitive advantage. Supplier selection and evaluation are key issues in the success of a competitive enterprise. Supplier selection for an enterprise is a typical multicriteria decision-making problem that includes qualitative and quantitative criteria. However, some input information can be missing or nonexistent in selecting suppliers, rendering it more difficult to choose the best supplier. To this end, the traditional supplier selection approach does not consider the ordered weights of the values of attributes, causing biased conclusions. Moreover, there is a significant amount of fuzzy and intuitionistic fuzzy information in real-world situations, for which the traditional approach in choosing the best supplier becomes no longer applicable. To solve these issues, this study proposes a novel supplier selection method, integrating the intuitionistic fuzzy weighted averaging method and the soft set with imprecise data, in identifying the best supplier in a supply chain. To illustrate our proposed method, a numerical example of the supplier selection problem is adopted. This paper also compares the results of the fuzzy weighted averaging, intuitionistic fuzzy weighted averaging, and intuitionistic fuzzy dependent aggregation operator methods in dealing with missing or nonexistent data. Based on our results, the proposed method is reasonable, effective, and better reflects real-world situations with regard to supplier selection.

Keywords

Supplier selection Intuitionistic fuzzy set Soft set Multi-criteria decision making Ordered weighted geometric averaging 

Notes

Acknowledgements

The authors would like to thank the Ministry of Science and Technology, Taiwan, for financially supporting this research under Contract Nos. MOST 105-2410-H-145-002 and MOST 106-2410-H-145-001.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Management SciencesR.O.C. Military AcademyKaohsiungTaiwan

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