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Intuitionistic fuzzy Bonferroni means.
1School of Economics and Management, Southeast University, Nanjing 210096, China. xu_zeshui@263.net
Summary
This study introduces the intuitionistic fuzzy Bonferroni mean (IFBM) to aggregate fuzzy numbers, extending the Bonferroni mean (BM) beyond crisp values. The IFBM is then applied to multicriteria decision-making problems.
Area of Science:
- Decision Sciences
- Fuzzy Mathematics
- Operations Research
Background:
- The Bonferroni mean (BM) aggregates crisp numbers and captures interrelationships between inputs.
- Existing literature primarily uses the Bonferroni mean for crisp number aggregation.
- There is a need to extend aggregation methods to handle more complex data types.
Purpose of the Study:
- To generalize the Bonferroni mean (BM) to intuitionistic fuzzy environments.
- To develop an intuitionistic fuzzy Bonferroni mean (IFBM) operator.
- To apply the IFBM to multicriteria decision-making (MCDM) problems.
Main Methods:
- Development of the intuitionistic fuzzy Bonferroni mean (IFBM).
- Analysis of special cases of the IFBM.
- Application of the weighted IFBM in MCDM.
Main Results:
- The proposed IFBM effectively aggregates intuitionistic fuzzy numbers.
- The weighted IFBM provides a robust method for MCDM.
- Numerical examples demonstrate the practical utility of the IFBM.
Conclusions:
- The IFBM is a valuable extension of the Bonferroni mean for intuitionistic fuzzy data.
- The IFBM enhances decision-making capabilities in complex, uncertain environments.
- This research opens avenues for further exploration of fuzzy aggregation techniques.
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