文献类型：期刊文献

英文题名：Multiple attribute decision method using similarity measure of cubic hesitant fuzzy sets

作者：Yong, Rui[1,2];Zhu, Aqin[1];Ye, Jun[1]

机构：[1]Shaoxing Univ, Key Lab Rock Mech & Geohazards Zhejiang Prov, Shaoxing, Peoples R China;[2]Shaoxing Univ, Civil Engn Fac, Shaoxing, Peoples R China

年份：2019

卷号：37

期号：1

起止页码：1075

外文期刊名：JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:000474715000092)、、EI(收录号：20193007226353)、Scopus(收录号：2-s2.0-85069478633)、WOS

基金：The study was funded by the National Natural Science Foundation of China (Nos. 41502300, 41427802, and 41572299), Zhejiang Provincial Natural Science Foundation (No. LQ16D020001), and Zhejiang Collaborative Innovation Center for Prevention and Control of Mountain Geological Hazards (Nos. PCMGH-2017-Z03, PCMGH-2017-Y-04, and PCMGH-2017-Y-05).

语种：英文

外文关键词：Cubic hesitant fuzzy set; multiple attribute decision-making; similarity measure; Jaccard measure

外文摘要：A cubic hesitant fuzzy set is a hybrid set which can express uncertain and hesitancy fuzzy information simultaneously. For multiple attribute decision-making problems in engineering practice, the complicated decision information is generally incomplete and indeterminate. Cubic hesitant fuzzy set can be a valuable tool for describing uncertain and hesitancy fuzzy information in uncertain decision environment. Nevertheless, no similarity measure has been used to solve decision-making problems under cubic hesitant environment in previous studies. This paper presents a Jaccard similarity measure between cubic hesitant fuzzy sets and investigates their properties. Then a multiple attribute decision-making method is developed based on the weighted Jaccard similarity measure under cubic hesitant environment. Using this method, the similarity measure values between the ideal alternative and each evaluated alternative are determined to obtain the ranking order of similarity measure values and the best alternative. An illustrative example of the selection problem of project alternatives is utilized to illustrate the application of the developed decision-making method. Finally, the validity of the proposed decision-making methodwas demonstrated based on the comparison of the decision-making results of the illustrative example with two distance-based similarity measures.