scholarly journals Linguistic Z-Number Bonferroni Mean and Linguistic Z-Number Geometric Bonferroni Mean Operators: Their Applications in Portfolio Selection Problems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 98742-98760
Author(s):  
Amir Hosein Mahmoodi ◽  
Seyed Jafar Sadjadi ◽  
Soheil Sadi-Nezhad ◽  
Roya Soltani ◽  
Farzad Movahedi Sobhani
Keyword(s):  
Portfolio Selection ◽  
Selection Problems ◽  
Bonferroni Mean ◽  
Geometric Bonferroni Mean

scholarly journals A Novel Multi-Criteria Group Decision-Making Approach Based on Bonferroni and Heronian Mean Operators under Hesitant 2-Tuple Linguistic Environment

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1489
Author(s):  
Shahzad Faizi ◽  
Wojciech Sałabun ◽  
Nisbha Shaheen ◽  
Atiq ur Rehman ◽  
Jarosław Wątróbski
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Weighted Average ◽  
Aggregation Operators ◽  
Bonferroni Mean ◽  
Operational Laws ◽  
Geometric Bonferroni Mean ◽  
The Individual ◽  
Heronian Mean

Ambiguous and uncertain facts can be handled using a hesitant 2-tuple linguistic set (H2TLS), an important expansion of the 2-tuple linguistic set. The vagueness and uncertainty of data can be grabbed by using aggregation operators. Therefore, aggregation operators play an important role in computational processes to merge the information provided by decision makers (DMs). Furthermore, the aggregation operator is a potential mechanism for merging multisource data which is synonymous with cooperative preference. The aggregation operators need to be studied and analyzed from various perspectives to represent complex choice situations more readily and capture the diverse experiences of DMs. In this manuscript, we propose some valuable operational laws for H2TLS. These new operational laws work through the individual aggregation of linguistic words and the collection of translation parameters. We introduced a hesitant 2-tuple linguistic weighted average (H2TLWA) operator to solve multi-criteria group decision-making (MCGDM) problems. We also define hesitant 2-tuple linguistic Bonferroni mean (H2TLBM) operator, hesitant 2-tuple linguistic geometric Bonferroni mean (H2TLGBM) operator, hesitant 2-tuple linguistic Heronian mean (H2TLHM) operator, and a hesitant 2-tuple linguistic geometric Heronian mean (H2TLGHM) operator based on the novel operational laws proposed in this paper. We define the aggregation operators for addition, subtraction, multiplication, division, scalar multiplication, power and complement with their respective properties. An application example and comparison analysis were examined to show the usefulness and practicality of the work.

scholarly journals Dual Generalized Nonnegative Normal Neutrosophic Bonferroni Mean Operators and Their Application in Multiple Attribute Decision Making

Information ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 201 ◽  
Author(s):  
Jiongmei Mo ◽  
Han-Liang Huang
Keyword(s):  
Decision Making ◽  
Score Function ◽  
Information Aggregation ◽  
Multiple Attribute Decision Making ◽  
Ranking Method ◽  
Bonferroni Mean ◽  
Operational Laws ◽  
Multiple Attribute ◽  
Geometric Bonferroni Mean ◽  
Neutrosophic Number

For multiple attribute decision making, ranking and information aggregation problems are increasingly receiving attention. In a normal neutrosophic number, the ranking method does not satisfy the ranking principle. Moreover, the proposed operators do not take into account the correlation between any aggregation arguments. In order to overcome the deficiencies of the existing ranking method, based on the nonnegative normal neutrosophic number, this paper redefines the score function, the accuracy function, and partial operational laws. Considering the correlation between any aggregation arguments, the dual generalized nonnegative normal neutrosophic weighted Bonferroni mean operator and dual generalized nonnegative normal neutrosophic weighted geometric Bonferroni mean operator were investigated, and their properties are presented. Here, these two operators are applied to deal with a multiple attribute decision making problem. Example results show that the proposed method is effective and superior.

scholarly journals A New Decision-Making Method for Stock Portfolio Selection Based on Computing with Linguistic Assessment

2009 ◽  
Vol 2009 ◽  
pp. 1-20 ◽  
Author(s):  
Chen-Tung Chen ◽  
Wei-Zhan Hung
Keyword(s):  
Decision Making ◽  
Portfolio Selection ◽  
Quantitative Data ◽  
Decision Makers ◽  
Stock Selection ◽  
Qualitative And Quantitative ◽  
Selection Problems ◽  
Different Types ◽  
Linguistic Assessment ◽  
Stock Portfolio

The purpose of stock portfolio selection is how to allocate the capital to a large number of stocks in order to bring a most profitable return for investors. In most of past literatures, experts considered the portfolio of selection problem only based on past crisp or quantitative data. However, many qualitative and quantitative factors will influence the stock portfolio selection in real investment situation. It is very important for experts or decision-makers to use their experience or knowledge to predict the performance of each stock and make a stock portfolio. Because of the knowledge, experience, and background of each expert are different and vague, different types of 2-tuple linguistic variable are suitable used to express experts' opinions for the performance evaluation of each stock with respect to criteria. According to the linguistic evaluations of experts, the linguistic TOPSIS and linguistic ELECTRE methods are combined to present a new decision-making method for dealing with stock selection problems in this paper. Once the investment set has been determined, the risk preferences of investor are considered to calculate the investment ratio of each stock in the investment set. Finally, an example is implemented to demonstrate the practicability of the proposed method.

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