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Medical health resources allocation evaluation in public health emergencies by an improved ORESTE method with linguistic preference orderings

Author

Listed:
  • Xunjie Gou

    (Sichuan University)

  • Xinru Xu

    (Sichuan University)

  • Fumin Deng

    (Sichuan University)

  • Wei Zhou

    (Yunnan University of Finance and Economics)

  • Enrique Herrera-Viedma

    (Andalusian Research Institute in Data Science and Computational Intelligence)

Abstract

As an important major public health emergency, COVID-19 broke out more than two years. At present, China has entered the post-epidemic era. However, it is still necessary to study the medical health resource allocation in public health emergencies. Therefore, the evaluation of medical health resources allocation is important. Firstly, we use two kinds of linguistic preference orderings (LPOs) to represent experts’ opinions when evaluating the medical health resources allocation in public health emergencies. Then, a novel ORESTE method with LPOs is developed to solve multiple criteria decision-making (MCDM) problems. Additionally, we apply the proposed ORESTE method to solve a practical MCDM problem involving the medical health resources allocation in public health emergencies. Finally, some comparative analyses among the proposed ORESTE method and some existing methods under a double hierarchy linguistic environment are set up, and some discussions are summarized to show the validity and applicability of the proposed novel ORESTE method.

Suggested Citation

  • Xunjie Gou & Xinru Xu & Fumin Deng & Wei Zhou & Enrique Herrera-Viedma, 2024. "Medical health resources allocation evaluation in public health emergencies by an improved ORESTE method with linguistic preference orderings," Fuzzy Optimization and Decision Making, Springer, vol. 23(1), pages 1-27, March.
    • Handle: RePEc:spr:fuzodm:v:23:y:2024:i:1:d:10.1007_s10700-023-09409-3
    DOI: 10.1007/s10700-023-09409-3
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