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ARTICLE
Abstract
This paper proposes a personalized comprehensive cloud-based method for heterogeneous multi-attribute group
decision-making (MAGDM), in which the evaluations of alternatives on attributes are represented by LTs (linguistic
terms), PLTSs (probabilistic linguistic term sets) and LHFSs (linguistic hesitant fuzzy sets). As an effective tool
to describe LTs, cloud model is used to quantify the qualitative evaluations. Firstly, the regulation parameters of
entropy and hyper entropy are defined, and they are further incorporated into the transformation process from LTs
to clouds for reflecting the different personalities of decision-makers (DMs). To tackle the evaluation information
in the form of PLTSs and LHFSs, PLTS and LHFS are transformed into comprehensive cloud of PLTS (C-PLTS)
and comprehensive cloud of LHFS (C-LHFS), respectively. Moreover, DMs’ weights are calculated based on the
regulation parameters of entropy and hyper entropy. Next, we put forward cloud almost stochastic dominance
(CASD) relationship and CASD degree to compare clouds. In addition, by considering three perspectives, a
comprehensive tri-objective programing model is constructed to determine the attribute weights. Thereby, a
personalized comprehensive cloud-based method is put forward for heterogeneous MAGDM. The validity of
the proposed method is demonstrated with a site selection example of emergency medical waste disposal in
COVID-19. Finally, sensitivity and comparison analyses are provided to show the effectiveness, stability, flexibility
and superiorities of the proposed method.
Keywords
Cite This Article
Mao, X., Wu, H., Wan, S. (2022). A Personalized Comprehensive Cloud-Based Method for Heterogeneous MAGDM and Application in COVID-19.
CMES-Computer Modeling in Engineering & Sciences, 131(3), 1751–1792.