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Article type: Research Article
Authors: Qu, Guohuaa; * | Li, Tianjiaoa | Zhao, Xiab; * | Qu, Weihuad; * | An, Qianyinga | Yan, Junaia; c
Affiliations: [a] College of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan, China | [b] Research Institute of Modern Enterprise Management of Guilin University of Technology, Guilin, China | [c] Cooperative innovation Center for Transition of Resource-based Economies, Shanxi University of Finance and Economics, Taiyuan, China | [d] Institute of Management and decision, Shanxi University, Taiyuan, China
Correspondence: [*] Corresponding authors. Xia Zhao, Guohua Qu and Weihua Qu, Business School, Guilin University of Technology, Guilin, 541004, China. Tel.: +86 3517666466; Fax: +86 3517666868; Shanxi University of Finance and Economics 030006, China. Shanxi University 030006, China. E-mails: [email protected] (X. Zhao), [email protected] (G. Qu), [email protected] (W. Qu).
Abstract: In this paper, a stochastic decision making method based on regret theory and group satisfaction is proposed with unknown attribute weights and dual hesitant fuzzy elements. Considering that the decision makers have different levels of satisfaction with the alternatives, first of all, according to the score function and the accuracy function of dual hesitant fuzzy elements, a novel group satisfaction degree function of dual hesitant fuzzy elements is defined. And then, an attribute weight optimization model based on the new group satisfaction degree of dual hesitant fuzzy elements is established and the Lagrange function is constructed to obtain the attribute weights. Secondly, on the basis of the regret theory, the regret and rejoice valued matrices of the program are given, and then the ranking values of each alternative can be obtained by combining with the weight of the attribute. Finally, a numerical example is given to illustrate the applicability and feasibility of the proposed method.
Keywords: Dual hesitant fuzzy element, regret theory, group satisfaction degree, stochastic decision making
DOI: 10.3233/JIFS-18667
Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6479-6488, 2018
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