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Multi-attribute group decision making based on sine trigonometric spherical fuzzy aggregation operators.

Muhammad QiyasSaleem AbdullahSaifullah KhanMuhammad Naeem
Published in: Granular computing (2021)
Spherical fuzzy set (SFS) is also one of the fundamental concepts for address more uncertainties in decision problems than the existing structures of fuzzy sets, and thus its implementation was more substantial. The well-known sine trigonometric function maintains the periodicity and symmetry of the origin in nature and thus satisfies the expectations of the experts over the multi parameters. Taking this feature and the significance of the SFSs into the consideration, the main objective of the article is to describe some reliable sine trigonometric laws for SFSs. Associated with these laws, we develop new average and geometric aggregation operators to aggregate the Spherical fuzzy numbers. Then, we presented a group decision-making strategy to address the multi-attribute group decision-making problem using the developed aggregation operators. To verify the value of the defined operators, a MAGDM strategy is provided along with an application for the selection of an authentic COVID-19 laboratory. Moreover, a comparative study is also performed to present the effectiveness of the developed approach.
Keyphrases
  • decision making
  • neural network
  • coronavirus disease
  • sars cov
  • mental health
  • primary care
  • randomized controlled trial
  • healthcare
  • machine learning
  • high resolution
  • deep learning
  • quality improvement