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

The q-rung orthopair picture fuzzy soft model offers a powerful framework for managing uncertainty through three membership grades: positive, neutral, and negative. In this study, we propose novel geometric aggregation operators, namely the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>q</mml:mi> <mml:mo>-</mml:mo> <mml:mtext>ROPF</mml:mtext> <mml:msub><mml:mi>S</mml:mi> <mml:mi>t</mml:mi></mml:msub> <mml:mtext>WG</mml:mtext></mml:mrow> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>q</mml:mi> <mml:mo>-</mml:mo> <mml:mtext>ROPF</mml:mtext> <mml:msub><mml:mi>S</mml:mi> <mml:mi>t</mml:mi></mml:msub> <mml:mtext>OWG</mml:mtext></mml:mrow> </mml:math> operator, and establish their theoretical properties to address a key gap in existing research. These operators are applied within a MADM framework using the CODAS method, demonstrated through a real-world stock investment selection problem. To assess performance, we conduct parameter analyses, including sensitivity tests across different values of q, and comparative evaluations with existing methods. Results confirm the robustness, adaptability, and superiority of the proposed approach, offering greater flexibility and reliability in decision-making. This work bridges a significant gap by advancing the practical application of aggregation operators in dynamic real-world scenarios. • To introduce the novel geometric aggregation operators within the context of q-rung orthopair picture fuzzy soft environment. • The proposed aggregation operators and CODAS method are tested on a real-world multi-attribute decision-making scenario concerning stock investment selection to evaluate their effectiveness and practical applicability. • In addition, we conduct various analysis tests with the existing methods to validate the robustness, flexibility, and superiority of the proposed model.