Double hierarchy hesitant fuzzy linguistic information based framework for personalized ranking of sustainable suppliers

Probabilistic double hierarchy linguistic Maclaurin symmetric mean-MultiCriteria Border Approximation area Comparison method for multi-criteria group decision making and its application in a selection of traditional Chinese medicine prescriptions

Traditional Chinese medicine (TCM) has gradually played an indispensable role in people's health maintenance, especially in the treatment of chronic diseases. However, there is always uncertainty and hesitation in the judgment and understanding of diseases by doctors, which affects the status recognition and optimal diagnosis and treatment decision-making of patients. In order to overcome the above problems, we lead into probabilistic double hierarchy linguistic term set (PDHLTS) to accurately describe language information in traditional Chinese medicine and make decisions. In this paper, a multi-criteria group decision making (MCGDM) model is constructed based on the MSM-MCBAC (Maclaurin symmetric mean-MultiCriteria Border Approximation area Comparison) method in the PDHL environment. Firstly, a PDHL weighted Maclaurin symmetric mean (PDHLWMSM) operator is proposed to aggregate the evaluation matrices of multiple experts. Then, combined with the BWM and maximizing deviation method, a comprehensive weight determination method is put forward to calculate the weights of criteria. Furthermore, we propose PDHL MSM-MCBAC method based on the Multi-Attributive Border Approximation area Comparison (MABAC) method and the PDHLWMSM operator. Finally, an example of a selection of TCM prescriptions is used and some comparative analyses are made to verify the effectiveness and superiority of this paper.

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

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.