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.

A decision analysis model for material selection using simple ranking process.

A large number of materials and various criteria fashion material selection problems as complex multi-criteria decision-making (MCDM) problems. This paper proposes a new decision-making method called the simple ranking process (SRP) to solve complex material selection problems. The accuracy of the criteria weights has a direct impact on the outcomes of the new method. In contrast to current MCDM methods, the normalization step has been eliminated from the SRP method as a potential source of producing incorrect results. The application of the method is appropriate for situations with high levels of complexity in material selection because it only considers the ranks of alternatives in each criterion. The first scenario of vital-immaterial mediocre method (VIMM) is used as a tool to derive criteria weights based on expert assessment. The result of SRP is compared with a number of MCDM methods. In order to evaluate the findings of analytical comparison, a novel statistical measure known as compromise decision index (CDI) is proposed in this paper. CDI revealed that the MCDM methods' outputs for solving the material selection could not be theoretically proven and requires to be evaluated through practice. As a result, the dependency analysis-an additional innovative statistical measure is introduced to demonstrate the reliability of MCDM methods by assessing its dependency on criteria weights. The findings demonstrated that SRP is extremely reliant on criteria weights and its reliability rises with the number of criteria, making it a perfect tool for solving challenging MCDM problems.

Identifying the leading retailer-based food waste causes in different perishable fast-moving consumer goods' categories: application of the F-LBWA methodology.

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.

Prioritizing causes and drivers of retail food waste through a fuzzy Dombi-Bonferroni operators-based best-worst approach: an emerging economy perspective.

The food waste hierarchy offers that prevention of the waste is the best solution to deal with this complex issue. Herein, to take a preventive action against food waste, this paper attempts to prioritize the key causes and drivers of retail food waste employing Dombi-Bonferroni operators-based fuzzy best-worst (DB-FBWM) framework. Drawing upon stakeholder theory and contingency theory, causes of retail food waste are categorized into six aspects and their 23 drivers. The results revealed that the "customer-driven" cause is found as the foremost aspect of retail food waste, whereas the "undesirable in-store customer behaviors" is the most vital driver. Identifying the causes and drivers of retail food waste through a reliable model can advance theory and allow building partnership among food supply chain stakeholders for sustainable waste management. The sensitivity and comparison analysis are further performed to confirm the solidity of the approach conducted.

Exploring factors affecting airport selection during the COVID-19 pandemic from air cargo carriers' perspective through the triangular fuzzy Dombi-Bonferroni BWM methodology.

The COVID-19 pandemic has created unexpected demand for air cargo in terms of rapid mobility of critical basic needs. Air cargo carriers aim to maximize their profits by taking advantage of the current demand and using their limited capacity in the right place. At this point, some of the qualifications of the airports in the places where demand plays a crucial role in this decision of the carriers. Thus, evaluating the factors considered in the airport selection for air cargo carriers during the COVID-19 period is curious. This study proposes a triangular fuzzy Dombi-Bonferroni best-worst method (BWM) framework with vast flexibility to establish the priority preferences of factors considered in selecting airports. The fuzzy BWM model becomes a superior decision support system by combining the Bonferroni mean operator's ability to consider interrelationships between attributes and the flexibility of the Dombi operator. In this sense, we highlight eighteen criteria based on five airport aspects: location, physical features, performance, costs, and reputation. Findings reveal that the foremost aspects are location and costs, whereas the most crucial factors are airport charges and handling charges. The study suggests that airports should follow a low-price policy for airport-related charges without compromising their sustainability to have a share of the increasing number of air cargo flights, especially during the COVID-19 period, when airline passenger flights are decreased. The study is crucial in deciding the strategy and policy of air cargo carriers and airports during the pandemic period.

An extended MAIRCA method using intuitionistic fuzzy sets for coronavirus vaccine selection in the age of COVID-19.

All over the world, the COVID-19 outbreak seriously affects life, whereas numerous people have infected and passed away. To control the spread of it and to protect people, appreciable vaccine development efforts continue with increasing momentum. Given that this pandemic will be in our lives for a long time, it is obvious that a reliable and useful framework is needed to choose among coronavirus vaccines. To this end, this paper proposes a new intuitionistic fuzzy extension of MAIRCA framework, named intuitionistic fuzzy MAIRCA (IF-MAIRCA) to assess coronavirus vaccines according to some evaluation criteria. Based on the group decision-making, the IF-MAIRCA framework both extracts the criteria weights and discovers the prioritization of the alternatives under uncertainty. In this work, as a case study, five coronavirus vaccines approved by the world's leading authorities are evaluated according to various criteria. The findings demonstrate that the most significant criteria considered in coronavirus vaccine selection are "duration of protection," "effectiveness of the vaccine," "success against the mutations," and "logistics," respectively, whereas the best coronavirus vaccine is AZD1222. Apart from this, the proposed model's robustness is verified with a three-phase sensitivity analysis.

MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services.

Assessing and ranking private health insurance companies provides insurance agencies, insurance customers, and authorities with a reliable instrument for the insurance decision-making process. Moreover, because the world's insurance sector suffers from a gap of evaluation of private health insurance companies during the COVID-19 outbreak, the need for a reliable, useful, and comprehensive decision tool is obvious. Accordingly, this article aims to identify insurance companies' priority ranking in terms of healthcare services in Turkey during the COVID-19 outbreak through a multi-criteria performance evaluation methodology. Herein, alternatives are evaluated and then ranked as per 7 criteria and assessments of 5 experts. Experts' judgments and assessments are full of uncertainties. We propose a Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) technique under an intuitionistic fuzzy environment to rank insurance companies. The outcomes yielded ten insurance companies ranking in terms of healthcare services in the era of COVID-19. The payback period, premium price, and network are determined as the most crucial factors. Finally, a comprehensive sensitivity analysis is performed to verify the proposed methodology's stability and effectiveness. The introduced approach met the insurance assessment problem during the COVID-19 pandemic very satisfactory manner based on sensitivity analysis findings.

Assessment of alternative fuel vehicles for sustainable road transportation of United States using integrated fuzzy FUCOM and neutrosophic fuzzy MARCOS methodology.

Greenhouse gas (GHG) emissions are one of the biggest challenging environmental problems globally, which leads countries to reduce their environmental impact in various disciplines. One of the most negative effects on the environment can be seen in the transportation area. It has been seen as a promising way to reduce emissions from transport with various alternative fuel vehicles (AFVs). This study aims to develop a multi-criteria decision-making (MCDM) methodology to prioritize the various AFVs for sustainable transport. The assessment of AFVs can be considered an MCDM problem due to the involvement of several conflicting criteria. We thus develop a novel multi-criteria decision-making methodology based on fuzzy Full Consistency Method (FUCOM-F) and neutrosophic fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (MARCOS) framework for the assessment of the AFVs. The proposed methodology is applied to prioritize the various AFVs in New Jersey, U.S. According to the findings, the most significant drivers for AFV selection are purchase cost, energy cost, and social benefits, respectively. The evaluation results also show that electric vehicles can serve as an effective approach to reducing carbon emissions for New Jersey. In addition, a comparative analysis is conducted to indicate the out-performance of the proposed multi-criteria methodology.

Sustainability assessment of existing onshore wind plants in the context of triple bottom line: a best-worst method (BWM) based MCDM framework.

Not only the steadily growing demand for electricity generation but also the environmental concerns in recent years have led to the belief in the importance of renewable energy. Wind is one of the most important renewable energy sources utilized in electricity generation for a sustainable environment. This paper deals with performance assessment for existing onshore wind plants in terms of triple bottom line of sustainability and aims to propose a structural methodology. Furthermore, the proposed framework is essentially formed through two stages: the first stage is the determination of relative weights for sustainability factors through the best-worst method (BWM) and the second stage is a sustainability performance assessment of the available 42 wind plants in Izmir, Turkey. According to the findings, the environmental dimension is the most significant, followed by the economic and social dimensions. The results also reveal that distance to protected areas is the most important factor among others in terms of sustainability performance and that the wind plants throughout the north side of Izmir have a higher sustainability performance. To validate the robustness and reliability of the introduced framework, a sensitivity analysis is also conducted. The proposed framework could be employed successfully in other scientific applications.

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction.

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs' direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron-genetic algorithms (MLP-GA) and multilayer perceptron-particle swarm optimization (MLP-PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP-PSO with population size 125, followed by MLP-GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.