Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Chao Zhang, Deyu Li, Xiangping Kang, Yudong Liang, Said Broumi and Arun Kumar Sangaiah
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Chao Zhang: Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
Deyu Li: Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
Xiangping Kang: Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
Yudong Liang: Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
Said Broumi: Laboratory of Information Processing, Faculty of Science Ben M’Sik, University Hassan II, Sidi Othman, Casablanca B.P 7955, Morocco
Arun Kumar Sangaiah: School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India

Mathematics, 2020, vol. 8, issue 2, 1-21

Abstract: In plenty of realistic situations, multi-attribute group decision-making (MAGDM) is ubiquitous and significant in daily activities of individuals and organizations. Among diverse tools for coping with MAGDM, granular computing-based approaches constitute a series of viable and efficient theories by means of multi-view problem solving strategies. In this paper, in order to handle MAGDM issues with interval-valued neutrosophic (IN) information, we adopt one of the granular computing (GrC)-based approaches, known as multigranulation probabilistic models, to address IN MAGDM problems. More specifically, after revisiting the related fundamental knowledge, three types of IN multigranulation probabilistic models are designed at first. Then, some key properties of the developed theoretical models are explored. Afterwards, a MAGDM algorithm for merger and acquisition target selections (M&A TSs) with IN information is summed up. Finally, a real-life case study together with several detailed discussions is investigated to present the validity of the developed models.

Keywords: multi-attribute group decision-making; granular computing; interval-valued neutrosophic information; multigranulation probabilistic models; merger and acquisition target selections (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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