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Deriving a ranking from hesitant fuzzy preference relations under group decision making.
IEEE Transactions on Cybernetics
|October 26, 2013
Summary
This study introduces novel ranking methods for group decision-making using hesitant fuzzy preference relations (HFPRs). It presents new models for priority derivation and consistency measurement, enhancing hesitant fuzzy set analysis.
Area of Science:
- Decision Sciences
- Fuzzy Mathematics
- Operations Research
Background:
- Group decision-making (GDM) involves aggregating preferences from multiple individuals.
- Hesitant fuzzy preference relations (HFPRs) handle uncertainty where decision-makers provide multiple possible preference values.
- Existing methods for comparing and aggregating hesitant fuzzy elements (HFEs) require normalization techniques.
Purpose of the Study:
- To develop novel ranking methods for GDM using HFPRs.
- To introduce new approaches for normalizing HFEs to facilitate computation and comparison.
- To establish robust consistency measures for HFPRs to ensure reliable decision outcomes.
Main Methods:
- Utilized α-normalization for developing a hesitant goal programming model to derive priorities from HFPRs.
- Employed β-normalization to create consistency measures and thresholds for HFPRs.
- Applied hesitant aggregation operators to aggregate preferences and generate final ranking results.
Main Results:
- A new hesitant goal programming model was developed based on α-normalization for priority derivation.
- Novel consistency measures and thresholds were established using β-normalization for HFPRs.
- The proposed methods effectively aggregate preferences and yield ranking results in GDM environments.
Conclusions:
- The study provides effective methods for ranking with HFPRs in GDM.
- Normalization techniques (α and β) are crucial for handling HFEs and ensuring consistency.
- The developed models and operators enhance the application of hesitant fuzzy sets in decision-making.
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