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Article type: Research Article
Authors: Wang, Ying | Sun, Bingzhen; * | Hu, Xiaoyuan
Affiliations: School of Economics and Management, Xidian University, Xi’an, China
Correspondence: [*] Corresponding author. Bingzhen Sun, School of Economics and Management, Xidian University, Xi’an, 710071, China. E-mail: bzsun@xidian.edu.cn.
Abstract: This paper proposes a new approach to uncertainty multiple attribute group decision problem with linguistic preference relation based on multigranulation probabilistic rough set and the Multimoora method. According to the classical Pawlak rough set and the neighborhood rough set, we present a multigranulation probabilistic fuzzy rough set based on neighborhood relation with linguistic preference information. We investigate the rough approximation of a crisp decision-making object and a fuzzy decision-making object under the framework of multigranulation rough set theory with linguistic preference inforamtion, respectively. That is, a multigranulation probabilistic rough set model and a multigranulation probabilistic fuzzy rough set model based on δ-neighborhood relation are established, respectively. Meanwhile, the proposed multigranulation probabilistic fuzzy rough set model is compared with the existing model and illustrate the superiority of the new model established. Furthermore, by combining the multigranulation probabilistic fuzzy rough set and the Multimoora method, a new method for multiple attribute group decision making with linguistic preference information is proposed. The decision-making procedure and the algorithm of the proposed method are also given. Finally, the effectiveness and validity of the proposed method is verified by investigating a multiple attribute group decision problem with the selection of suppliers in the context of e-commerce.
Keywords: Neighborhood relationship, multigranulation probability rough set, multiple attribute group decision-making, Multimoora, linguistic preference information
DOI: 10.3233/JIFS-190290
Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4171-4194, 2019
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