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Article type: Research Article
Authors: Si, Guangsena | Liao, Huchanga; * | Yu, Dejianb | Llopis-Albert, Carlosc
Affiliations: [a] Business School, Sichuan University, Chengdu, China | [b] School of Information, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China | [c] Universitat Politècnica de València, C/ Camí de Vera s/n, Valencia, Spain
Correspondence: [*] Corresponding author. Huchang Liao, Business School, Sichuan University, Chengdu 610064, China. E-mail: [email protected].
Abstract: The hesitant fuzzy linguistic term sets can retain the completeness of linguistic information elicitation by assigning a set of possible linguistic terms to a qualitative variable. However, sometimes experts cannot make sure that the objects attain these possible linguistic terms but only provide the degrees of confidence to express their hesitant cognition. Given that the interval numbers can denote the possible membership degrees that an object belongs to a set, it is suitable and convenient to provide an interval-valued index to measure the degree of a linguistic variable to a given hesitant fuzzy linguistic term set. Inspired by this idea, we introduce the concept of interval-valued 2-tuple hesitant fuzzy linguistic term set (IV2THFLTS) based on the interval number and the hesitant fuzzy linguistic term set. Then, we define some interval-valued 2-tuple hesitant fuzzy linguistic aggregation operators. Afterwards, to overcome the instability of subjective weights, we propose a method to compute the weights of attributes. For the convenience of application, a method is given to solve the multiple attribute decision making problems with IV2THFLTSs. Finally, a case study is carried out to validate the proposed method, and some comparisons with other methods are given to show the advantages of the proposed method.
Keywords: Hesitant fuzzy linguistic term sets, interval numbers, interval-valued 2-tuple hesitant fuzzy linguistic term set, aggregation operators, weight determining method, oversea investment evaluation
DOI: 10.3233/JIFS-171967
Journal: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 4225-4236, 2018
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