Operational funds allocation through stock investment based on CODAS method and q-ROPFS aggregation operators

Area of Science:

• Decision Sciences
• Fuzzy Set Theory
• Computational Intelligence

Background:

• Managing uncertainty is crucial in decision-making.
• Existing fuzzy models have limitations in handling complex uncertainty.
• The q-rung orthopair picture fuzzy soft model provides a robust framework for uncertainty management.

Purpose of the Study:

• To introduce novel geometric aggregation operators ($q-\text{ROPF}{S}_t\text{WG}$ and $q-\text{ROPF}{S}_t\text{OWG}$) within the q-rung orthopair picture fuzzy soft environment.
• To establish the theoretical properties of these new operators.
• To address a gap in the practical application of aggregation operators in dynamic real-world scenarios.

Main Methods:

• Development of novel geometric aggregation operators: $q-\text{ROPF}{S}_t\text{WG}$ and $q-\text{ROPF}{S}_t\text{OWG}$.
• Application of these operators within a Multi-Attribute Decision-Making (MADM) framework using the Complex Decision Analysis (CODAS) method.
• Validation through a real-world stock investment selection problem, including parameter analysis and comparative evaluations.

Main Results:

• The proposed $q-\text{ROPF}{S}_t\text{WG}$ and $q-\text{ROPF}{S}_t\text{OWG}$ operators demonstrate robustness and adaptability.
• The integrated approach shows superiority and greater flexibility compared to existing methods in decision-making.
• Effective application in a practical stock investment selection scenario confirmed the model's utility.

Conclusions:

• The novel geometric aggregation operators enhance the q-rung orthopair picture fuzzy soft model's capabilities.
• The proposed method provides a reliable and flexible tool for complex decision-making problems.
• This research advances the practical application of aggregation operators in dynamic and uncertain environments.