Group decision-making approach under multi (Q, N)-soft multi granulation rough model

In this paper, we propose three novel hybrid models for multi-attribute decision-making, namely, multi Q-fuzzy N-soft sets [in short, multi (Q, N)-soft sets], multi (Q, N)-soft rough sets and multi (Q, N)-soft multi-granulation rough sets. We present some properties of these models and illustrate with examples. Further, we define multi (Q, N)-soft rough approximation operators in terms of multi (Q, N)-soft relations. We describe the properties of the lower and upper multi (Q, N)-soft rough approximation operators. Moreover, we present respective new approaches of the proposed models to decision-making problems in an algorithmic format. We apply the presented methods to some real-world decision-making problems for the representation of multi-attribute data, selection of suitable medicine sample. Finally, we discuss a comparison between the presented approaches and certain existing methods.