Generalization of Maximizing Deviation and TOPSIS Method for MADM in Simplified Neutrosophic Hesitant Fuzzy Environment

被引:41
|
作者
Akram, Muhammad [1 ]
Naz, Sumera [2 ]
Smarandache, Florentin [3 ]
机构
[1] Univ Punjab, Dept Math, New Campus, Lahore 54590, Pakistan
[2] Govt Coll Women Univ Faisalabad, Dept Math, Punjab 38000, Pakistan
[3] Univ New Mexico Math, Sci Dept, 705 Gurley Ave, Gallup, NM 87301 USA
来源
SYMMETRY-BASEL | 2019年 / 11卷 / 08期
关键词
simplified neutrosophic hesitant fuzzy set; multi-attribute decision-making; maximizing deviation; TOPSIS; DECISION-MAKING APPROACH; SIMILARITY MEASURES; SETS;
D O I
10.3390/sym11081058
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
With the development of the social economy and enlarged volume of information, the application of multiple-attribute decision-making (MADM) has become increasingly complex, uncertain, and obscure. As a further generalization of hesitant fuzzy set (HFS), simplified neutrosophic hesitant fuzzy set (SNHFS) is an efficient tool to process the vague information and contains the ideas of a single-valued neutrosophic hesitant fuzzy set (SVNHFS) and an interval neutrosophic hesitant fuzzy set (INHFS). In this paper, we propose a decision-making approach based on the maximizing deviation method and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to solve the MADM problems, in which the attribute weight information is incomplete, and the decision information is expressed in simplified neutrosophic hesitant fuzzy elements. Firstly, we inaugurate an optimization model on the basis of maximizing deviation method, which is useful to determine the attribute weights. Secondly, using the idea of the TOPSIS, we determine the relative closeness coefficient of each alternative and based on which we rank the considered alternatives to select the optimal one(s). Finally, we use a numerical example to show the detailed implementation procedure and effectiveness of our method in solving MADM problems under simplified neutrosophic hesitant fuzzy environment.
引用
收藏
页数:26
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