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Soft Computing

, Volume 22, Issue 22, pp 7445–7461 | Cite as

A scientific decision-making framework for supplier outsourcing using hesitant fuzzy information

  • R. Krishankumar
  • K. S. Ravichandran
  • K. K. Murthy
  • A. B. Saeid
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  • 31 Downloads

Abstract

Supply chain management (SCM) is an attractive area for research which has seen tremendous growth in the past decades. From the literature we observe that, supplier outsourcing (SO) is a highly explored research field in SCM which lacks significant scientific contribution. The major concern in SO is the decision makers’ (DMs) viewpoint which are often vague and imprecise. To better handle such imprecision, in this paper, we propose a new two-stage decision-making framework called TSDMF, which uses hesitant fuzzy information as input. In the first stage, the DMs’ preferences are aggregated using a newly proposed simple hesitant fuzzy-weighted geometry operator, which uses hesitant fuzzy weights for better understanding the importance of each DM. Following this, in the second stage, criteria weights are estimated using newly proposed hesitant fuzzy statistical variance method and finally, a new ranking method called three-way hesitant fuzzy VIKOR (TWHFV) is proposed by extending the VIKOR ranking method to hesitant fuzzy environment. This ranking method uses three categories viz., cost, benefit and neutral along with Euclid distance for its formulation. The practicality of the proposed TSDMF is verified by demonstrating a supplier outsourcing example in an automobile factory. The robustness of TWHFV is realized by using sensitivity analysis and other strengths of TSDMF are discussed by comparison with another framework.

Keywords

Supplier outsourcing Hesitant fuzzy VIKOR Standard variance Aggregation Decision making 

Notes

Acknowledgements

We the authors thank the funding agency, University Grants Commission (UGC) for their financial support through Rajiv Gandhi National Fellowship (RGNF) scheme under the award number: F./2015-17/RGNF-2015-17-TAM-83. We also thank the Department of Science and Technology (DST), India for their financial aid in setting up a cloud environment under the FIST programme (Award Number: SR/FST/ETI-349/2013). We also express our heartfelt thanks to SASTRA University for offering us an excellent infrastructure to carry out our research work. Finally, we express our sincere thanks to the editor and to the anonymous reviewers for their constructive comments.

Compliance with ethical standards

Conflict of interest

All authors of this research paper declare that, there is no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ICT, School of ComputingSASTRA UniversityThanjavurIndia
  2. 2.Department of Pure Mathematics, Faculty of Mathematics and ComputerShahid Bahonar University of KermanKermanIran

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