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Multiattribute Group Decision-Making Method Based on Quaternary Connection Number of Cloud Models

Author

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  • Huabin Cheng
  • Yingchun Chen
  • Ping Xiong
  • Amandeep Kaur

Abstract

Based on the advantages of cloud theory and the connection number approach in handling uncertain information, this paper established a new quaternary connection number using the domain of interval corresponding to the centroid of a cloud model’s expectation curve. Consequently, based on the cloud model theory of transformation between qualitative and quantitative uncertainties, this study suggests a model describing uncertain information more precisely by combining the advantages of quaternary connection numbers with three digital aspects of the cloud model. We defined the weighted nearness degree of the new connection number and gave a solution for finding the weight of the weighted nearness degree given that a three-parameter interval number can more precisely represent the expert’s true intention than a classical interval number. The method of multiattribute group decision-making based on a cloud model’s quaternary connection number was developed using a novel methodology to find the evaluation-index weight. According to a comparative analysis, the existing membership cloud gravity center (MCGC) method is nothing more than an exception to our proposed decision-making technique. It was also demonstrated that the proposal may present a more complete picture of experts’ overall evaluations and communicate their preferences by adjusting the quaternion’s uncertain parameters, making the group decision-making approach more widely applicable to a certain extent. The method was used to test a vessel’s counterflooding capabilities to ensure its practicality and supremacy.

Suggested Citation

  • Huabin Cheng & Yingchun Chen & Ping Xiong & Amandeep Kaur, 2022. "Multiattribute Group Decision-Making Method Based on Quaternary Connection Number of Cloud Models," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, September.
  • Handle: RePEc:hin:jnlmpe:8101024
    DOI: 10.1155/2022/8101024
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