Multicriteria group decision making for COVID-19 testing facility based on picture cubic fuzzy aggregation...

Area of Science:

• Fuzzy Mathematics
• Decision Science
• Computational Intelligence

Background:

• Information aggregation is crucial for decision-making, especially with complex data structures like cubic and picture fuzzy numbers.
• Picture cubic fuzzy sets offer a generalized framework to manage increased uncertainty and ambiguity in decision problems.

Purpose of the Study:

• To introduce novel Hamacher geometric aggregation operators for picture cubic fuzzy information.
• To develop and apply a multicriteria group decision-making (MCGDM) algorithm using these operators for selecting COVID-19 testing facilities.

Main Methods:

• Development of picture cubic fuzzy Hamacher weighted geometric, hybrid geometric, and order weighted geometric aggregation operators.
• Exploration of the properties of these newly defined operators.
• Construction of an MCGDM algorithm tailored for the picture cubic fuzzy environment.

Main Results:

• The proposed aggregation operators demonstrate effectiveness in handling picture cubic fuzzy information.
• The developed MCGDM algorithm successfully selected an authentic laboratory for COVID-19 testing.
• Comparative analysis confirmed the superiority of the proposed operators over existing methods.

Conclusions:

• The novel picture cubic fuzzy Hamacher geometric aggregation operators are effective for MCGDM problems.
• The proposed approach provides a robust method for selecting COVID-19 testing facilities amidst uncertainty.
• This research advances fuzzy set theory applications in real-world decision-making scenarios.