Adaptive Multi-Criteria group Decision-Making for optimized crop selection using the p,q- quasirung orthopair fuzzy hybrid aggregation operator

This study introduces the $p,q-$ Quasirung Orthopair Fuzzy Hybrid Aggregation ($p,q-$ QOFHA) operator, designed to enhance multi-criteria group decision-making (MCGDM) under uncertainty and imprecise information. Unlike traditional aggregation operators, which lack flexibility in handling dynamic environmental factors, the proposed $p,q-$ QOFHA operator incorporates two adjustable parameters, $p$ and $q$, enabling adaptive control over decision-making. These parameters allow decision-makers to account for environmental changes, weather conditions, and external factors affecting agricultural productivity, making the approach more robust and practical for real-world applications. The study applies the proposed method to an optimized crop selection problem, evaluating seven crop alternatives (wheat, rice, maize, sugarcane, soybean, barley, and cotton) based on five critical criteria (soil fertility, water availability, temperature, market demand, and sustainability). The entropy-based weighting approach is used to determine criteria importance. Unlike traditional fuzzy MCDM models, which treat uncertainty rigidly, the $p,q-$ QOFHA operator dynamically adjusts membership and non-membership values, ensuring a more realistic and flexible decision-making process. Sensitivity analysis further demonstrates that adjusting $p$ and $q$ enables for adaptive responses to changing agricultural conditions, outperforming conventional fuzzy aggregation approaches. The findings highlight the superiority of the proposed $p,q-$ QOFHA operator in handling uncertainty and adapting to external influences, making it a powerful tool for sustainable agricultural decision-making. This study provides a foundation for applying advanced fuzzy MCDM models in climate adaptation, resource management, and other complex decision-making scenarios where dynamic environmental factors play a crucial role.