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Investment Selection Based on Bonferroni Mean under Generalized Probabilistic Hesitant Fuzzy Environments

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  • Wenying Wu

    (School of Management, Hefei University of Technology, Hefei 230009, Anhui, China
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, Anhui, China)

  • Zhiwei Ni

    (School of Management, Hefei University of Technology, Hefei 230009, Anhui, China
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, Anhui, China)

  • Feifei Jin

    (School of Business, Anhui University, Hefei 230601, Anhui, China)

  • Jian Wu

    (Researching Center of Social Security, Wuhan University, Wuhan 430072, Hubei, China)

  • Ying Li

    (School of Management, Hefei University of Technology, Hefei 230009, Anhui, China
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, Anhui, China)

  • Ping Li

    (School of Management, Hefei University of Technology, Hefei 230009, Anhui, China
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, Anhui, China)

Abstract

In investment selection problems, the existence of contingency and uncertainty may result in the loss of attribute information. Then, how to make proper investment decision-making will be a tricky proposition. In this work, a multiattribute group decision making (MAGDM) method based on the generalized probabilistic hesitant fuzzy Bonferroni mean (GPHFBM) operator is constructed, which enables decision-makers to select the proper parameters in decision-making process. Firstly, the GPHFBM operator is proposed by combining the Bonferroni mean operator and Archimedean norm. Secondly, five excellent properties of the GPHFBM operator are discussed in detail. In view of applications, we further develop some special aggregation operators for GPHFBM with the various values of parameters b , d and additive operators g ( t ). Finally, we propose a probabilistic hesitant fuzzy MAGDM method based on the GPHFBM operator to analyze the aggregated information. A case study of the investment of social insurance funds is given to depict the validity and reasonability of the proposed method. Ultimately, the company X 4 is selected as the investment company with the best comprehensive indicator.

Suggested Citation

  • Wenying Wu & Zhiwei Ni & Feifei Jin & Jian Wu & Ying Li & Ping Li, 2021. "Investment Selection Based on Bonferroni Mean under Generalized Probabilistic Hesitant Fuzzy Environments," Mathematics, MDPI, vol. 9(1), pages 1-21, January.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:1:p:107-:d:475204
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    References listed on IDEAS

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    Cited by:

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