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

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Abstract

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.

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Funding

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).

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Authors and Affiliations

  1. College of Economics and Management, Beijing University of Technology, City, Beijing, 100124, China

    Zaoli Yang

  2. College of Management, Beijing Union University, City, Beijing, 100101, China

    Jinping Chang

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  1. Zaoli Yang
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  2. Jinping Chang
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Correspondence to Jinping Chang.

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Yang, Z., Chang, J. Solving dynamic normal distribution stochastic decision-making problems based on time degree and vertical projection distance. Pers Ubiquit Comput 22, 1153–1163 (2018). https://doi.org/10.1007/s00779-018-1148-z

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  • DOI: https://doi.org/10.1007/s00779-018-1148-z

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