491
Views
42
CrossRef citations to date
0
Altmetric
Articles

Multi-criteria decision-making approaches based on distance measures for linguistic hesitant fuzzy sets

, ORCID Icon &
Pages 661-675 | Received 04 Mar 2015, Accepted 26 Apr 2016, Published online: 05 Jan 2018
 

Abstract

Linguistic hesitant fuzzy sets (LHFSs), which can be used to both represent decision-makers’ qualitative preferences and reflect their hesitancy and inconsistency, have attracted much attention due to their flexibility and efficiency. In this paper, some distance-based approaches for resolving multi-criteria decision-making (MCDM) problems with linguistic hesitant fuzzy information are introduced. To begin, a new order relationship between LHFSs, based on the defined LHFSs, is presented. Then, distance measures for LHFSs are proposed, which include the generalised, Hamming, and Euclidean distance measures. Additionally, some approaches for handling MCDM problems with linguistic hesitant fuzzy information are proposed, which are based on the TOPSIS, VIKOR, and TODIM methods, as well as the proposed distance measures. Finally, an illustrative example is provided to show the feasibility and usability of the methods, which are then compared with the existing method.

Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for their very helpful comments and suggestions.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 277.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.