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Hesitant triangular fuzzy information aggregation operators based on Bonferroni means and their application to
Chunyong Wang1, Qingguo Li1, Xiaoqiang Zhou2
1College of Mathematics and Econometrics, Hunan University Changsha, Hunan 410082, China.
Thescientificworldjournal
|August 21, 2014
Summary
This study introduces new methods for multiple attribute decision-making (MADM) using hesitant triangular fuzzy sets. The developed aggregation operators enhance decision-making processes in complex, uncertain environments.
Area of Science:
- Decision Sciences
- Fuzzy Mathematics
- Operations Research
Background:
- Multiple attribute decision-making (MADM) problems often involve uncertainty and vagueness.
- Hesitant fuzzy sets allow for multiple membership degrees, but triangular fuzzy numbers add complexity.
- Existing methods may not fully capture the nuances of hesitant triangular fuzzy information.
Purpose of the Study:
- To introduce hesitant triangular fuzzy elements and their operational laws.
- To develop novel aggregation operators for hesitant triangular fuzzy information using Bonferroni means.
- To apply these operators to solve MADM problems in a hesitant triangular fuzzy environment.
Main Methods:
- Definition and operational laws for hesitant triangular fuzzy elements.
- Development of hesitant triangular fuzzy aggregation operators based on Bonferroni means.
- Application of these operators to MADM problems with illustrative examples.
Main Results:
- Established foundational definitions and operational laws for hesitant triangular fuzzy elements.
- Introduced new aggregation operators with demonstrable properties, generalizing existing ones.
- Successfully applied the novel operators to solve MADM problems, validating their effectiveness.
Conclusions:
- The proposed hesitant triangular fuzzy aggregation operators are effective for MADM problems.
- The developed method provides a practical and efficient approach for decision-making under uncertainty.
- This research contributes to the advancement of decision-making theories in fuzzy environments.
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