A decomposed fuzzy based fusion of decision-making and metaheuristic algorithm to select best unmanned aerial vehicle in agriculture 4.0 era

Abstract

As the world embraces sustainable and smart solutions, agriculture is evolving through rapid technological advancements. Unmanned Aerial Vehicles (UAVs) are transforming smart farming, particularly for smallholder farmers, by reducing costs, saving time, and improving efficiency of agricultural tasks. This study aims to introduce a comprehensive group decision-making framework for selecting the most suitable UAV for agricultural purposes. Traditional Multi-Criteria Decision-Making (MCDM) methods face challenges with intricacies, non-linearity, limited exploration of solution space and weight distortion during defuzzification. To address these issues, this study introduces a novel Decomposed Fuzzy-based Non-Linear (DFNL) optimization model within Analytical Hierarchy Process (AHP), which directly extracts subjective crisp weights from DF-decisions. A hybrid metaheuristic algorithm is then proposed to solve this model efficiently. Additionally, objective weights are calculated using the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method and qualitative data, enhancing the accuracy of the decision-making process. For ranking the UAV alternatives, the full Multiplicative form of the Multi-Objective Optimization by Ratio Analysis (MULTIMOORA) method is applied. The effectiveness of the proposed methodology is demonstrated through two extensive examples and validated via a case study focusing on the Indian subcontinent. Sensitivity analysis confirms its robustness and stability. The findings and novelties are supported by comparing with other extant models. This fusion of group decision-making methods and metaheuristic algorithms improves weight accuracy, reduces manual complexity, and adapts to uncertainty, offering policymakers actionable insights and a tailored approach for UAV selection.