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    A novel hybrid decision-making framework based on modified fuzzy analytic network process and fuzzy best–worst method
    (Springer Science and Business Media Deutschland GmbH, 2024) Khanmohammadi, Ehsan; Azizi, Maryam; Talaie, HamidReza; Ecer, Fatih; Tirkolaee, Erfan Babaee
    The development of decision-making frameworks is essential to improve the accuracy and efficiency of selecting the best options in complex scenarios. This research develops a novel efficient decision-making framework based on Fuzzy Analytic Network Process and Fuzzy Best–Worst Method. The motivation behind this work stems from the recognized challenges associated with establishing consistent Pairwise Comparison Matrices, a critical concern in the application of paired comparison analysis approaches. The primary objective is to overcome Pairwise Comparison Matrices inconsistencies, which can compromise the reliability of decision-making processes. To address this challenge, the study introduces a modified approach where variables are selectively compared with the best and worst counterparts, deviating from conventional methods that involve comprehensive comparisons among all variables. Innovatively, the research develops a nonlinear mathematical model-based methodology, to extract variable weights from Fuzzy Pairwise Comparison Matrices. The motivation behind this model is to elicit crisp weights with a reduced number of judgments from decision-makers, streamlining the decision-making process and mitigating the burden on stakeholders. The applicability and validity of the proposed approach are demonstrated through practical examples, including the resolution of a production line selection problem and sustainable supplier selection. By addressing real-world challenges, the study establishes the practical relevance and effectiveness of the developed decision-making method. Ultimately, the findings indicate that the research methodology is not only robust but also flexible, showcasing its adaptability to different decision-making scenarios. The findings reveal that in our suggested model, the reduction in pairwise comparisons is approximately 50% when compared to traditional methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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    The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era
    (Springer, 2023) Ecer, Fatih; Ogel, Ilkin Yaran; Krishankumar, Raghunathan; Tirkolaee, Erfan Babaee
    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.