文献类型：期刊文献

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

作者：Yang, Zaoli[1];Chang, Jinping[2]

第一作者：Yang, Zaoli

通讯作者：Chang, JP[1]

机构：[1]Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China;[2]Beijing Union Univ, Coll Management, Beijing 100101, Peoples R China

第一机构：Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Management, Beijing 100101, Peoples R China.|[1141755]北京联合大学管理学院;[11417]北京联合大学;

年份：2018

卷号：22

期号：5-6

起止页码：1153-1163

外文期刊名：PERSONAL AND UBIQUITOUS COMPUTING

收录：;EI(收录号：20181905166080);Scopus(收录号：2-s2.0-85046401233);WOS:【SCI-EXPANDED(收录号:WOS:000452549100027)】；

基金：This research was supported by the Natural Science Foundation of China (Grant No. 71704007) and the China Postdoctoral Science Foundation Funded Project (Grant No. 2016M600889).

语种：英文

外文关键词：Dynamic normal distribution stochastic; Time degree; Vertical projection distance; Possibility degree

摘要：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.