q-Rung orthopair fuzzy dynamic aggregation operators with time sequence preference for dynamic decision-making

Area of Science:

• Fuzzy Mathematics
• Decision Science
• Operations Research

Background:

• The q-rung orthopair fuzzy set (q-ROPFS) framework allows for richer fuzzy information representation compared to existing models.
• Information aggregation is crucial for multi-criteria decision-making, with emerging interest in q-ROPFS applications.
• Existing aggregation operators may not fully capture the dynamic and temporal aspects of decision-making data.

Purpose of the Study:

• To introduce novel dynamic aggregation operators for q-rung orthopair fuzzy numbers (q-ROPFNs).
• To develop a decision-making method for multi-period problems using these operators and ideal solutions.
• To address decision-making challenges involving time-varying fuzzy information.

Main Methods:

• Development of two new aggregation operators: dynamic q-rung orthopair fuzzy Einstein weighted averaging (DQROPFEWA) and dynamic q-rung orthopair fuzzy Einstein weighted geometric (DQROPFEWG).
• Utilizing Einstein aggregation operators for effective information fusion within the q-ROPFS framework.
• Application of a decision-making method based on ideal solutions for multi-period problems.

Main Results:

• The proposed DQROPFEWA and DQROPFEWG operators effectively aggregate q-ROPFNs across multiple time periods.
• A novel method for multi-period decision-making using these operators is presented and validated.
• A numerical example demonstrates the application in assessing the impact of COVID-19 on daily life.

Conclusions:

• The developed dynamic q-rung orthopair fuzzy aggregation operators offer enhanced capabilities for handling complex decision-making scenarios.
• The proposed decision-making technique provides an effective framework for multi-stage and dynamic decision analysis.
• The study highlights the broad applicability of these methods in real-world dynamic decision-making problems.