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Four types of grey β-covering models and their applications

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  • Atef, Mohamed
  • Liu, Sifeng

Abstract

To handle the limitations of a fuzzy β-neighbourhood, many researchers apply this notion to different structures to allow them to make a suitable decision in some real problems. In this article, we introduce the notions of grey β-neighbourhood and grey complementary β-neighbourhood, and then we establish the grey β-covering approximation space (GβCAS). The relevant characteristics are also examined. Furthermore, we construct two new GβCAS models to combine the definitions of a grey β-neighbourhood and a grey complementary β-neighbourhood and explain their relations. In addition, employing grey β-neighbourhoods, we investigate four types of β-neighbourhoods and use them to build four rough approximation models. Hence, in order to give a new approach to MADM in grey β-covering approximation space, we establish a novel methodology based on GCβAS models called multi-attribute grey decision making (MAGDM). Finally, with comparisons to current studies, a numerical example is given to demonstrate the feasibility and usefulness of our suggested methodologies.

Suggested Citation

  • Atef, Mohamed & Liu, Sifeng, 2024. "Four types of grey β-covering models and their applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 223(C), pages 108-129.
    • Handle: RePEc:eee:matcom:v:223:y:2024:i:c:p:108-129
    DOI: 10.1016/j.matcom.2024.03.033
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    References listed on IDEAS

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    1. Yun Kang & Shunxiang Wu & Yuwen Li & Wei Weng, 2017. "New and improved: grey multi-granulation rough sets," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(12), pages 2575-2589, September.
    2. Maghrabie, Hesham F. & Beauregard, Yvan & Schiffauerova, Andrea, 2019. "Grey-based Multi-Criteria Decision Analysis approach: Addressing uncertainty at complex decision problems," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 366-379.
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