RESUMO
Smart agriculture is gaining a lot of attention recently, owing to technological advancement and promotion of sustainable habits. Unmanned aerial vehicles (UAVs) play a crucial role in smart agriculture by aiding in different phases of agriculture. The contribution of UAVs to sustainable and precision agriculture is a critical and challenging issue to be taken into account, particularly for smallholder farmers in order to save time and money, and improve their agricultural skills. Thence, this study targets to propose an integrated group decision-making framework to determine the best agricultural UAV. Previous studies on UAV evaluation, (i) could not model uncertainty effectively, (ii) weights of experts are not methodically determined; (iii) importance of experts and criteria types are not considered during criteria weight calculation, and (iv) personalized ranking of UAVs is lacking along with consideration to dual weight entities. Herein, nine critical selection criteria are identified, drawing upon the relevant literature and experts' opinions, and five extant UAVs are considered for evaluation. To circumvent the gaps, in this work, a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection. Specifically, methodical estimation of experts' weights is achieved by presenting the regret measure. Further, weighted logarithmic percentage change-driven objective weighting (LOPCOW) technique is formulated for criteria weight calculation, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy. The findings show that the foremost criteria in agricultural UAV selection are "camera," "power system," and "radar system," respectively. Further, it is inferred that the most promising UAV is the DJ AGRAS T30. Since the applicability of UAV in agriculture will get inevitable, the developed framework can be an effective decision support system for farmers, managers, policymakers, and other stakeholders.
RESUMO
Waste in fast-moving consumer goods (FMCGs) is a tremendous economic and ethical issue for retailers and the rest of society. Due to methodological weaknesses, previous studies are inadequate in prioritizing fundamental causes and drivers of retail food waste (RFW) in this context. This research explores the peculiar causes and drivers of RFW concerning different perishable FMCG categories. This research employs the fuzzy level-based weight assessment (F-LBWA) methodology to provide a robust and effective decision-making tool to retailers responsible for preventing waste in their stores. This research categorizes the causes and drivers into different product categories giving insight into the reasons and drivers that need more attention than others for each product category. The findings reveal that inappropriate buying/delivery is the most significant cause of waste for fruit and vegetables, dairy products, fresh meat, fish and seafood, and baked products, whereas improper storage is the most critical cause of waste for frozen food. The present work ensures practical implications for developing product category-specific waste management policies to improve retailers' efficiency, competitiveness, and profitability. For a developing country like Turkey, the applicable insights of this research can also serve all supply chain members and policymakers to prevent food waste through partnership.
