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

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 hesitant fuzzy sets). As an effective tool to describe LTs, cloud model is used quantify qualitative evaluations. Firstly, regulation parameters entropy hyper defined, they further incorporated into transformation process from clouds reflecting different personalities decision-makers (DMs). To tackle evaluation information form LHFSs, PLTS LHFS transformed (C-PLTS) (C-LHFS), respectively. Moreover, DMs’ weights calculated based entropy. Next, we put forward almost stochastic dominance (CASD) relationship CASD degree compare clouds. In addition, considering three perspectives, tri-objective programing constructed determine attribute weights. Thereby, MAGDM. The validity proposed demonstrated with site selection example emergency medical waste disposal COVID-19. Finally, sensitivity comparison analyses provided show effectiveness, stability, flexibility superiorities method.