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

Wu, Wenying; Ni, Zhiwei; Jin, Feifei; Wu, Jian; Li, Ying; Li, Ping

doi:10.3390/math9010107

Open AccessArticle

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

by

Wenying Wu

^1,2,

Zhiwei Ni

^1,2,*,

Feifei Jin

³,

Jian Wu

⁴,

Ying Li

^1,2 and

Ping Li

^1,2

¹

School of Management, Hefei University of Technology, Hefei 230009, Anhui, China

²

Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, Anhui, China

³

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

⁴

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

^*

Author to whom correspondence should be addressed.

Mathematics 2021, 9(1), 107; https://doi.org/10.3390/math9010107

Submission received: 1 December 2020 / Revised: 28 December 2020 / Accepted: 31 December 2020 / Published: 5 January 2021

(This article belongs to the Section D2: Operations Research and Fuzzy Decision Making)

Download

Browse Figures

Versions Notes

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.

Keywords: probabilistic hesitant fuzzy set; Bonferroni mean operator; Archimedean t-norm and s-norm; generalized probabilistic hesitant fuzzy Bonferroni mean operator; investment selection

Share and Cite

MDPI and ACS Style

Wu, W.; Ni, Z.; Jin, F.; Wu, J.; Li, Y.; Li, P. Investment Selection Based on Bonferroni Mean under Generalized Probabilistic Hesitant Fuzzy Environments. Mathematics 2021, 9, 107. https://doi.org/10.3390/math9010107

AMA Style

Wu W, Ni Z, Jin F, Wu J, Li Y, Li P. Investment Selection Based on Bonferroni Mean under Generalized Probabilistic Hesitant Fuzzy Environments. Mathematics. 2021; 9(1):107. https://doi.org/10.3390/math9010107

Chicago/Turabian Style

Wu, Wenying, Zhiwei Ni, Feifei Jin, Jian Wu, Ying Li, and Ping Li. 2021. "Investment Selection Based on Bonferroni Mean under Generalized Probabilistic Hesitant Fuzzy Environments" Mathematics 9, no. 1: 107. https://doi.org/10.3390/math9010107

APA Style

Wu, W., Ni, Z., Jin, F., Wu, J., Li, Y., & Li, P. (2021). Investment Selection Based on Bonferroni Mean under Generalized Probabilistic Hesitant Fuzzy Environments. Mathematics, 9(1), 107. https://doi.org/10.3390/math9010107

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI