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A Personalized comprehensive cloud-Based Method for Heterogeneous MAGDM and Application in cOVID-19

computer Modeling in Engineering & Sciences 2022年第6期131卷 1751-1792页

作者： Xiaobing Mao Hao Wu Shuping Wan School of Information Management Jiangxi University of Finance and EconomicsNanchang330013China

This paper proposes a personalized comprehensive cloud-based method for heterogeneous multi-attribute groupdecision-making (MAGDM), in which the evaluations of alternatives on attributes are represented by LTs (linguisticterms), PLTSs (probabilistic linguistic term sets) and lhfss (linguistic hesitant fuzzy sets). As an eective toolto describe LTs, cloud model is used to quantify the qualitative evaluations. Firstly, the regulation parameters ofentropy and hyper entropy are dened, and they are further incorporated into the transformation process from LTsto clouds for reecting the dierent personalities of decision-makers (DMs). To tackle the evaluation informationin 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 theregulation 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, acomprehensive tri-objective programing model is constructed to determine the attribute weights. Thereby, apersonalized comprehensive cloud-based method is put forward for heterogeneous MAGDM. The validity ofthe proposed method is demonstrated with a site selection example of emergency medical waste disposal incOVID-19. Finally, sensitivity and comparison analyses are provided to show the eectiveness, stability, exibilityand superiorities of the proposed method.

关键词： Heterogeneous MAGDM regulation parameter c-PLTS c-lhfs cA

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