q-Rung Orthopair Fuzzy Rough Einstein Aggregation Information-Based EDAS Method: Applications in Robotic Agrifarming.

Area of Science:

• Information Science
• Decision Science
• Fuzzy Mathematics

Background:

• Fuzzy rough sets are essential for handling uncertainty.
• Existing models face limitations with complex, multi-criteria decision-making.
• There is a need for robust methods to manage imprecise and uncertain information.

Purpose of the Study:

• To introduce the novel q-rung orthopair fuzzy rough set (q-ROFRS) concept.
• To develop new q-ROFR Einstein aggregation operators.
• To propose a decision-making algorithm integrating entropy, aggregation, and EDAS for uncertain environments.

Main Methods:

• Hybridization of q-rung orthopair fuzzy rough sets and rough sets.
• Development of q-ROFR Einstein weighted averaging and geometric aggregation operators.
• Integration of entropy measures, aggregation operators, and the EDAS methodology.

Main Results:

• A novel q-ROFRS framework and its basic operations are established.
• New Einstein aggregation operators with desirable properties are introduced.
• A decision-making algorithm is developed and validated through a case study in agriculture.

Conclusions:

• The proposed q-ROFRS model and decision-making algorithm are efficient and reliable for handling uncertainty.
• The developed technique offers a superior approach for multicriteria group decision-making problems.
• The study demonstrates practical applicability in real-world decision-making scenarios.