Hesitant interval neutrosophic linguistic set and its application in multiple attribute decision making View Full Text


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Article Info

DATE

2019-04

AUTHORS

Jun Ye

ABSTRACT

Motivated by the ideas of interval neutrosophic linguistic sets and hesitant fuzzy sets (HFSs), this paper proposes the concept of hesitant interval neutrosophic linguistic sets by combining the three concepts of the HFS, interval neutrosophic set, and linguistic set and defines the operational laws of hesitant interval neutrosophic linguistic elements (HINLEs) and the score, accuracy, and certainty functions for HINLEs. Then, a hesitant interval neutrosophic linguistic weighted average (HINLWA) operator and a hesitant interval neutrosophic linguistic weighted geometric (HINLWG) operator are developed to aggregate the hesitant interval neutrosophic linguistic information. Moreover, some desirable properties of the two operators are investigated. A decision-making method based on the HINLWA and HINLWG operators is developed to handle multiple attribute decision-making problems, in which evaluation values of each alternative with respect to attributes are expressed by the form of HINLEs, under hesitant interval neutrosophic linguistic environment. Finally, an illustrative example about investment alternatives is given to demonstrate the application of the developed method. More... »

PAGES

667-678

References to SciGraph publications

  • 2016-01-11. Recent advances in multiple criteria decision making techniques in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • 2014-12. Multiple attribute decision-making method based on single-valued neutrosophic normalized weighted Bonferroni mean in NEURAL COMPUTING AND APPLICATIONS
  • 2013-12. An intuitionistic fuzzy weighted OWA operator and its application in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • 2017-06. Multiple attribute group decision making based on interval neutrosophic uncertain linguistic variables in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
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    http://scigraph.springernature.com/pub.10.1007/s13042-017-0747-8

    DOI

    http://dx.doi.org/10.1007/s13042-017-0747-8

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