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

Hafiz Muhammad Athar Farid1, Muhammad Riaz1, Vladimir Simic2,3

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Peerj. Computer Science
|March 4, 2024
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Summary

This study introduces dynamic q-rung orthopair fuzzy aggregation operators for multi-period decision-making. These novel operators enhance fuzzy information processing for complex, time-varying problems.

Keywords:
Aggregation operatorsDynamic decision-making

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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.