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An efficiency-based interval type-2 fuzzy multi-criteria group decision making for makeshift hospital selection.
Chen, Ze-Hui; Wan, Shu-Ping; Dong, Jiu-Ying.
Afiliación
  • Chen ZH; School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, China.
  • Wan SP; School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, China.
  • Dong JY; School of Statistics, Jiangxi University of Finance and Economics, Nanchang 330013, China.
Appl Soft Comput ; 115: 108243, 2022 Jan.
Article en En | MEDLINE | ID: mdl-34899106
ABSTRACT
Since makeshift hospitals have strong ability in blocking the spread of the virus, how to design some methods to select the reasonable sites of makeshift hospitals is vitally important for containing COVID-19. This paper investigates an efficiency-based multi-criteria group decision making (MCGDM) method by combining the best-worst method (BWM) and data envelopment analysis (DEA) in trapezoidal interval type-2 fuzzy (TrIT2F) environment. This MCGDM method is called TrIT2F-BWM-DEA, where the TrIT2F-BWM is used to determine the weights of criteria and decision-makers, and the TrIT2F-DEA is employed to rank alternatives by measuring their overall efficiencies. Based on cut set theory, the expectation and average expectation (AE) of TrIT2FSs are successively defined. To solve three key issues in the development of the TrIT2F-BWM, this paper proposes a flexible ranking relation of TrIT2FSs to transform the TrIT2F constraints, initiates an efficient theorem to normalize the TrIT2F weights, and designs an input-based consistency ratio to check the reliability of the determined weights. A fully TrIT2F-DEA model is originally built to measure the TrIT2F efficiencies of alternatives. The alternatives are finally ranked according to the AEs of alternatives' TrIT2F efficiencies. A site selection case of Fangcang hospitals and some comparative analyses are provided to confirm the validity and merits of the proposed TrIT2F-BWM-DEA.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Appl Soft Comput Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Appl Soft Comput Año: 2022 Tipo del documento: Article País de afiliación: China