Next Article in Journal
Application of Fuzzy Analytic Hierarchy Process to Underground Mining Method Selection
Previous Article in Journal
An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry
Open AccessFeature PaperArticle

New Distance Measures for Dual Hesitant Fuzzy Sets and Their Application to Multiple Attribute Decision Making

Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(2), 191; https://doi.org/10.3390/sym12020191
Received: 24 December 2019 / Revised: 15 January 2020 / Accepted: 15 January 2020 / Published: 23 January 2020
Multiple attribute decision making (MADM) is full of uncertainty and vagueness due to intrinsic complexity, limited experience and individual cognition. Representative decision theories include fuzzy set (FS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), dual hesitant fuzzy set (DHFS) and so on. Compared with IFS and HFS, DHFS has more advantages in dealing with uncertainties in real MADM problems and possesses good symmetry. The membership degrees and non-membership degrees in DHFS are simultaneously permitted to represent decision makers’ preferences by a given set having diverse possibilities. In this paper, new distance measures for dual hesitant fuzzy sets (DHFSs) are developed in terms of the mean, variance and number of elements in the dual hesitant fuzzy elements (DHFEs), which overcomes some deficiencies of the existing distance measures for DHFSs. The proposed distance measures are effectively applicable to solve MADM problems where the attribute weights are completely unknown. With the help of the new distance measures, the attribute weights are objectively determined, and the closeness coefficients of each alternative can be objectively obtained to generate optimal solution. Finally, an evaluation problem of airline service quality is conducted by using the distance-based MADM method to demonstrate its validity and applicability. View Full-Text
Keywords: multiple attribute decision making; distance measure; dual hesitant fuzzy set; determination of attribute weights; TOPSIS multiple attribute decision making; distance measure; dual hesitant fuzzy set; determination of attribute weights; TOPSIS
Show Figures

Figure 1

MDPI and ACS Style

Wang, R.; Li, W.; Zhang, T.; Han, Q. New Distance Measures for Dual Hesitant Fuzzy Sets and Their Application to Multiple Attribute Decision Making. Symmetry 2020, 12, 191.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop