Hesitant fuzzy linguistic multicriteria decision-making method based on generalized prioritized aggregation operator.

Area of Science:

• Decision Sciences
• Operations Research
• Fuzzy Logic

Background:

• Multicriteria decision-making (MCDM) problems often involve uncertainty and complex criteria.
• Hesitant fuzzy sets and linguistic term sets offer frameworks for handling uncertainty in decision-making.
• Existing MCDM methods may not adequately address scenarios with prioritized criteria and linguistic uncertainty.

Purpose of the Study:

• To introduce hesitant fuzzy linguistic numbers (HFLNs) for representing uncertain linguistic information in MCDM.
• To propose a novel approach for solving MCDM problems with prioritized criteria using HFLNs.
• To develop and apply prioritized aggregation operators for HFLNs in decision-making contexts.

Main Methods:

• Development of new operations and a comparison method for hesitant fuzzy linguistic numbers (HFLNs).
• Application of linguistic scale functions to HFLNs.
• Design of two prioritized aggregation operators and a generalized prioritized aggregation operator for HFLNs.

Main Results:

• The proposed generalized prioritized aggregation operator effectively handles MCDM problems with prioritized criteria and HFLNs.
• New operations and comparison methods for HFLNs are established.
• The developed method demonstrates effectiveness and feasibility through an illustrative example.

Conclusions:

• The proposed approach provides a robust framework for MCDM problems involving prioritized criteria and hesitant fuzzy linguistic information.
• The new aggregation operators enhance the ability to manage linguistic uncertainty and criterion priorities in decision-making.
• The method offers a valuable alternative to existing approaches for complex decision scenarios.