Solving dynamic normal distribution stochastic decision-making problems based on time degree and vertical projection distance

How to effectively aggregate time-series information has long been a significant issue in the field of decision-making method and decision support system. This paper studies a dynamic normal distribution stochastic decision-making method that is based on the time degree and vertical projection distance. A dynamic normal distribution number weighted arithmetic average (DNDNWAA) operator is introduced, and a time sequence weight calculation model is constructed that fully considers the subjective preference of the historical information of the decision-maker. An attribute weight-determining model based on vertical projection distance is presented against the characteristics of normally distributed stochastic variables. The original dynamic normal distribution stochastic decision-making information is aggregated via the aggregation operator under the normally distributed stochastic variables. The aggregated comprehensive stochastic decision-making information based on stochastic probability distribution theory is converted into interval numbers, and the interval number possibility degree model is applied to provide a solution ordering result. Finally, the validity and rationality of the method proposed in this paper are verified by analyzing numerical examples. The proposed method can guide decision-makers to make better decisions in dynamic random information environment.