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    A Personalized Comprehensive Cloud-Based Method for Heterogeneous MAGDM and Application in COVID-19

    Xiaobing Mao, Hao Wu, Shuping Wan*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1751-1792, 2022, DOI:10.32604/cmes.2022.019501

    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… More >

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