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

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Listed:
  • 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
    China National Tobacco Corporation, Shanxi, Taiyuan 030000, 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)

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.

Suggested Citation

  • Chao Zhang & Deyu Li & Xiangping Kang & Yudong Liang & Said Broumi & Arun Kumar Sangaiah, 2020. "Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information," Mathematics, MDPI, vol. 8(2), pages 1-21, February.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:223-:d:318497
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    References listed on IDEAS

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    2. Chao Zhang & Deyu Li & Yimin Mu & Dong Song, 2018. "A Pythagorean Fuzzy Multigranulation Probabilistic Model for Mine Ventilator Fault Diagnosis," Complexity, Hindawi, vol. 2018, pages 1-19, October.
    3. Florentin Smarandache, 2019. "Refined Neutrosophy and Lattices vs. Pair Structures and YinYang Bipolar Fuzzy Set," Mathematics, MDPI, vol. 7(4), pages 1-16, April.
    4. Zeshui Xu, 2015. "Uncertain Multi-Attribute Decision Making," Springer Books, Springer, edition 127, number 978-3-662-45640-8, December.
    5. Songtao Shao & Xiaohong Zhang, 2019. "Measures of Probabilistic Neutrosophic Hesitant Fuzzy Sets and the Application in Reducing Unnecessary Evaluation Processes," Mathematics, MDPI, vol. 7(7), pages 1-23, July.
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