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The global system of supply chains has been dramatically disrupted over the last years due to the outbreak of the COVID‐19 pandemic. In these current challenging times, this paper proposes a methodological approach for managing dependence and uncertainty in dynamic industrial scenarios. A detailed study of epidemic effects is carried out according to an operational management‐based perspective. We proceed by analyzing connections among effects and risks potentially leading to significant supply chain disturbances through a multicriteria approach. Risks and effects are weighted by applying the Analytic Network Process (ANP). Weighted risks are then assumed as criteria for selecting the most suitable contingency strategy. To this aim, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is able to rank a set of strategies by addressing and quantifying uncertainty. A case study on the sector of the automotive industry is implemented to validate the proposed methodological approach. [ FROM AUTHOR] Copyright of Mathematical Methods in the Applied Sciences is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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It often occurs that after a multi-criteria decision is made, the decision maker becomes unsure as to whether they have made the best decision. This doubt arises because the criteria being considered do not carry the same weightings. This instability is relevant to the consideration of possible future events, such as a possible recession following the COVID-19 outbreak, which may affect the criteria weightings. The stratified multi-criteria decision-making method (SMCDM) has been proposed to address this issue. This method suggests the consideration of a number of states in the decision-making process. In each state, the weightings of the criteria are different depending on which event or which combination of events are being considered. The states are associated with transition probabilities that are used to compute the optimal weightings of the criteria. This paper suggests approaches to compute the transition probabilities. Moreover, the consideration of several events in SMCDM results in a great number of states and this would be a time consuming and error prone process. Hence, the incremental enlargement characteristic of the concept of stratification (CST) is added to SMCDM in order to reduce the large numbers of states to a manageable quantity. IEEE
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COVID-19 is a worldwide health threat that has resulted in a significant number of deaths and complicated healthcare management issues. To prevent the COVID-19 pandemic, there is a need to choose a safe and most effective vaccine. Several Multi-criteria Decision-Making (MADM) techniques and approaches have been selected to choose the optimal probable options. The purpose of this article is to deliver divergence measures for fuzzy sets. To validate these measures, some of the properties were also proved. The Multi-criteria Decision-Making method is employed to rank and hence select the best vaccine out of available alternatives. The proposed research allows the ranking of different vaccines based on specified criteria in a fuzzy environment to aid in the selection process. The results suggest that the proposed model provides a realistic way to select the best vaccine from the vaccines available. A case study on the selection of the best COVID-19 vaccine and its experimental results using fuzzy sets are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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One of the most important challenges for decision-makers and investors is location selection, which may be assessed using multicriteria decision-making (MCDM) methodologies. Problems with picking a location include deciding between alternative locations, analyzing alternatives, and identifying the best location for a hospital. Because they analyze options with multiple perspectives in terms of numerous competing criteria, MCDM approaches are useful instruments for solving decision-making challenges. The fuzzy set theory (FST), which represents uncertainty in human beliefs, may be effectively used with MCDM approaches to produce more sensitive, tangible, and accurate findings in this context. A hybrid fuzzy multi-criteria decision model (FMCDM) is proposed to find the optimal location based on a combination of factors. In the first stage, the Fuzzy Analytical Hierarchy Process (FAHP) is used to estimate the relative criteria classification through the evaluation process. In the second stage, the fuzzy technique of order preference using similarities to the perfect solution (FTOPSIS) is applied to rank the possible alternative sites. The findings from this study indicate that integrate FAHP and FTOPSIS is the most often used FMCDM approach in Aleppo for selecting the best location for a new hospital.
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Multicriteria Decision Making (MCDM) has a huge role to play while ruling out one suitable alternative among a pool of alternatives governed by predefined multiple criteria. Some of the factors like imprecision, lack of information/data, etc., which are present in traditional MCDM processes have showcased their lack of efficiency and hence eventually it has paved the ways for the development of Fuzzy multicriteria decision making (FMCDM). In FMCDM processes, the decision makers can model most of the real-life phenomena by fuzzy information-based preferences. The availability of a wide literature on similarity measure (SM) emphasizes the vital role of SM of generalized fuzzy numbers (GFNs) to conduct accurate and precise decision making in FMCDM problems. Despite having few advantages, most of the existing approaches possessed a certain degree of counter intuitiveness and discrepancies. Thus, we have attempted to propose a novel SM for generalized trapezoidal fuzzy numbers (GTrFNs) which could deliberately overcome the impediments associated with the earlier existing approaches. Moreover, a meticulous comparative study with the existing approaches is also presented. This paper provides us with an improved method to obtain the similarity values between GTrFNs and the proposed SM consists of calculating the prominent features of fuzzy numbers such as expected value and variance. We use fourteen different sets of GTrFNs, to compare the fruition of the present approach with the existing SM approaches. Furthermore, to show the utility and applicability of our proposed measure, we illustrate few practical scenarios such as the launching of an electronic gadget by a company, a problem of medical diagnosis and finally, a proper anti-virus mask selection in light of the recent COVID-19 pandemic. The obtained results with our proposed SM, for the mentioned FMCDM problems, are analytically correct and they depict the efficiency and novelty of the present article.
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Coronavirus disease (COVID-19), which is caused by SARS-CoV-2, has been announced as a global pandemic by the World Health Organization (WHO), which results in the collapsing of the healthcare systems in several countries around the globe. Machine learning (ML) methods are one of the most utilized approaches in artificial intelligence (AI) to classify COVID-19 images. However, there are many machine-learning methods used to classify COVID-19. The question is: which machine learning method is best over multi-criteria evaluation? Therefore, this research presents benchmarking of COVID-19 machine learning methods, which is recognized as a multi-criteria decision-making (MCDM) problem. In the recent century, the trend of developing different MCDM approaches has been raised based on different perspectives;however, the latest one, namely, the fuzzy decision by opinion score method that was produced in 2020, has efficiently been able to solve some existing issues that other methods could not manage to solve. because of the multiple criteria decision-making problem and because some criteria have a conflict problem. The methodology of this research was divided into two main stages. The first stage related to identifying the decision matrix used eight different ML methods on chest X-ray (CXR) images and extracted a new decision matrix so as to assess the ML methods. The second stage related to FDOSM was utilized to solve the multiple criteria decision-making problems. The results of this research are as follows: (1) The individual benchmarking results of three decision makers are nearly identical;however, among all the used ML methods, neural networks (NN) achieved the best results. (2) The results of the benchmarking group are comparable, and the neural network machine learning method is the best among the used methods. (3) The final rank is more logical and closest to the decision-makers' opinion. (4) Significant differences among groups' scores are shown by our validation results, which indicate the authenticity of our results. Finally, this research presents many benefits, especially for hospitals and medical clinics, with a view to speeding up the diagnosis of patients suffering from COVID-19 using the best machine learning method. © 2023 University of Baghdad-College of Science. All rights reserved.
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In this paper, we propose a method based on multicriteria classification and a dominancebased rough set approach (DRSA) to support teachers in decision making. The objective is to use teachers' knowledge and preferences to identify ‘atrisk students', i.e. students who are likely to drop out, and ‘leader students', i.e. students who are likely to help their peers, in distance learning. The proposed method is composed of two phases: phase I builds collective decision rules from teachers' preferences, and phase II classifies students into two decision classes: ‘atrisk students' and ‘leader students'. This method was designed, tested, and validated in higher education, with teachers who have acquired rich experience in teaching in online-synchronous mode since the Covid-19 pandemic. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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Havoc, brutality, economic breakdown, and vulnerability are the terms that can be rightly associated with COVID-19, for the kind of impact it is having on the whole world for the last two years. COVID-19 came as a nightmare and it is still not over yet, changing its form factor with each mutation. Moreover, each unpredictable mutation causes more severeness. In the present article, we outline a decision support algorithm using Generalized Trapezoidal Intuitionistic Fuzzy Numbers (GTrIFNs) to deal with various facets of COVID-19 problems. Intuitionistic fuzzy sets (IFSs) and their continuous counterparts, viz., the intuitionistic fuzzy numbers (IFNs), have the flexibility and effectiveness to handle the uncertainty and fuzziness associated with real-world problems. Although a meticulous amount of research works can be found in the literature, a wide majority of them are based mainly on normalized IFNs rather than the more generalized approach, and most of them had several limitations. Therefore, we have made a sincere attempt to devise a novel Similarity Measure (SM) which considers the evaluation of two prominent features of GTrIFNs, which are their expected values and variances. Then, to establish the superiority of our approach we present a comparative analysis of our method with several other established similarity methods considering ten different profiles of GTrIFNs. The proposed SM is then validated for feasibility and applicability, by elaborating a Fuzzy Multicriteria Group Decision Making (FMCGDM) algorithm and it is supportedby a suitable illustrative example. Finally, the proposed SM approach is applied to tackle some significant concerns due to COVID-19. For instance, problems like the selection of best medicine for COVID-19 infected patients; proper healthcare waste disposal technique; and topmost government intervention measures to prevent the COVID-19 spread, are some of the burning issues which are handled with our newly proposed SM approach.
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Investment in education technology (EdTech) is a complex decision problem for universities during the post-Covid era. With the objective to assess the quality and adoptability of education supply chain, a novel analytical evaluation model approach is proposed, based on quality function deployment and combinative distance-based assessment. To deal with uncertainty in the evaluation process, fuzzy theory is integrated into the model. To establish the house of quality matrix, technology-based stakeholders' requirements were identified and classified in four dimensions: economic and financial, technology adoption, sustainability, competencies. Moreover, nine supplier criteria were assumed. Based on expert evaluations, the results suggest that financial credit and supplier collaboration are the most prominent attributes to evaluate suppliers, while environmental commitment is sorted as the least important criterion. The results reveal that the three dominant suppliers, which provide the best response to the identified criteria, are providers of cloud service technology. © 2022
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The proceedings contain 79 papers presendted at a virtual meeting. The special focus in this conference is on Recent Advances in Mechanical Engineering Research and Development. The topics include: Firmware of Indigenous and Custom-Built Flexible Robots for Indoor Assistance;Automation of AM Via IoT Towards Implementation of e-logistics in Supply Chain for Industry 4.0;Evaluation and Optimization of Process Parameter for Surface Roughness of 3D-Printed PETG Specimens Using Taguchi Method at Constant Printing Temperature;Evaluation of Preventive Activities of COVID-19 Using Multi-criteria Decision Making Method;mechanical Characterization of Concrete with Rice Husk-Based Biochar as Sustainable Cementitious Admixture;Ranking of Barriers for SSCM Implementation in Indian Textile Industries;Framework to Monitor Vehicular GHG Footprint;solution to Real-Time Problem in Shifter Knob Assembly at Automobile Manufacturing Industry;performance of Chemical Route-Synthesized SnO2 Nanoparticles;a Numerical Study to Choose the Best Model for a Bladeless Wind Turbine;Effect of Tissue Properties on the Efficacy of MA on Lungs;effect of Process Parameters and Coolant Application on Cutting Performance of Centrifugal Cast Single Point Cutting Tools;Study and Analysis of Thermal Barrier Application of Lanthanum Oxide Coated SS-304 Steel;recovery of Iron Values from Blast Furnace Gas Cleaning Process Sludge by Medium Intensity Magnetic Separation Method;fatigue Analysis of Rectangular Plate with a Circular Cut-Out;protection of Vital Facilities from the Threat of External Explosion Using D3o Material;investigation on Coefficient of Heat Transfer Through Impact of Engine Vibration;electrical Modulus and Conductivity Study of Styrene-Butadiene Rubber/Barium Hexaferrite Flexible Polymer Dielectrics;preface.
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In this paper, we focus on the management of risks in the maritime transport sector after the spread of the pandemic Covid-19. We propose an integrated strategy to determine the best action decision to manage the most important risks. This strategy is based on two stages: the first stage consists on detecting the most serious risks using the AMDEC method and proposing a set of corrective actions for each of these risks;the second stage is responsible to determine the most appropriate action about the set of proposed alternatives using the AHP multi-criteria decision-making approach. Finally, to validate the proposed strategy, real data are collected from a Tunisian maritime transport company and the obtained results show the effectiveness of the proposed integrated method. © 2022 IEEE.
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The spread of COVID-19 has led many people to turn to O2O platforms to buy daily supplies leading to a boom in the O2Oe-commerce industry. How to improve the service quality of O2O platforms to attract more customers has become an important concern for service providers. This study differs from previous statistical analysis studies in that it applies the data mining methodology to extract the key factors that affect the service quality of O2O e-commerce platforms. A hybrid multi-criteria decision-making method is then utilized to obtain the influence relationships and weights of the dimensions and criteria. The results suggest that privacy security, and reliability have a positive impact on social interaction, recommendation quality, efficiency and empathy. Empathy, social interaction and recommendation quality are the three most important factors for evaluating the service quality of O2Oe-commerce platforms. Finally, the theoretical implications and management implications based on the findings are discussed. © 2022 IEEE.
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The office of social affairs has been helping poor or marginalized families through social aid in dealing with poverty. This social aid emphasizes decreasing the poverty rate and economic recovery affected by the COVID-19 pandemic. However, the selection process in the previous year evaluated many families as aid recipient candidates through a conventional process selected by an officer. It would be vulnerable to subjectivity in assessment. Therefore, we attempted to develop a hybrid Multiple Criteria Decision-Making (MCDM) methodology to apply it to this particular decision-making process. This methodology amalgamated the DEMATEL method and the EDAS method with different tasks. Firstly, the DEMATEL method decided the feasible criteria weights automatically based on the proficient decision-maker assessment in estimating a pairwise criteria comparison. Secondly, the EDAS applied the criteria weight values to determine alternatives rank order based on the value of the appraisal score. After comparing with the VIKOR method, the hybrid DEMATEL-EDAS method demonstrated the stability and capability in dealing with the different influence parameters on the final results. © 2022 IEEE.
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Mesenchymal stem cell (MSC) transfusion has shown promising results in treating COVID-19 cases despite the limited availability of these MSCs. The task of prioritizing COVID-19 patients for MSC transfusion based on multiple criteria is considered a multi-attribute decision-analysis (MADA) problem. Although literature reviews have assessed the prioritization of COVID-19 patients for MSCs, issues arising from imprecise, unclear and ambiguous information remain unresolved. Compared with the existing MADA methods, the robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy-weighted zero inconsistency (FWZIC) is proven. This study adopts and integrates FDOSM and FWZIC in a homogeneous Fermatean fuzzy environment for criterion weighting followed by the prioritization of the most eligible COVID-19 patients for MSC transfusion. The research methodology had two phases. The decision matrices of three COVID-19 emergency levels (moderate, severe, and critical) were adopted based on an augmented dataset of 60 patients and discussed in the first phase. The second phase was divided into two subsections. The first section developed Fermatean FWZIC (F-FWZIC) to weigh criteria across each emergency level of COVID-19 patients. These weights were fed to the second section on adopting Fermatean FDOSM (F-FDOSM) for the purpose of prioritizing COVID-19 patients who are the most eligible to receive MSCs. Three methods were used in evaluating the proposed works, and the results included systematic ranking, sensitivity analysis, and benchmarking checklist.
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These days, the appraisal of the COVID-19 vulnerability has become a difficult errand for the whole world. The COVID-19 administration dynamic issue frequently includes numerous elective arrangements clashing standards. In this paper, we present a multi-criteria decision-making (MCDM) procedure based on the fuzzy VIKOR method to survey the COVID-19 vulnerability in the state of Assam, India. The trapezoidal fuzzy number is utilized to evaluate the rating of the loads for the set-up models. We have observed environment, social, and Medical factors after observing the spread of COVID-19. To study and to have comments, a committee of five experts has been formed from a different region of Assam to observe and comment to identify Coronavirus's weakest factors. For a better survey, we have divided the state into four areas namely Rural Area, Urban Area, Market Area in Rural Area, and Market Area in Urban Area. The current research looked at how the fuzzy VIKOR selects provinces for urgent adaptation needs differently than a traditional MCDM technique. © 2022 - IOS Press. All rights reserved.
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Our purpose in this paper is to develop an integrated multicriteria evaluation methodology for assessing the impact of COVID-19 in the 27 countries of the European Union. Initially, a specialized and comprehensive set of normalized criteria metrics that capture several dimensions of the pandemic, such as infection, mortality, recovery and testing rates, is carefully specified. Then, by means of a well-established and intuitive weighting system, which directly takes into consideration the experts’ preferences, the gravity of each criterion is determined properly. Next, two of the most popular multicriteria ranking techniques, i.e. the TOPSIS and the PROMETHEE II, are simultaneously exploited in order to derive and integrate the obtained evaluations. Moreover, the value of the suggested decision support system is enriched through the introduction of a novel element in the field of multicriteria analysis, i.e. the ‘2-dimensions evaluation plane’, a data visualization concept which provides rich information to the decision maker, by fruitfully blending the ranking results of the utilized multicriteria methods. The validity of the proposed approach is verified through an indicative illustrative application on Coronavirus data for the European Union countries, deriving a wide spectrum of insightful conclusions. Finally, the flexibility of the suggested framework is also stressed, since it can be fully customized, both in terms of the selected criteria and its weights, and run repetitively under a specific time-step frequency, according to the evolution dynamics and implications of the underlying pandemic.
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Today, the tourism industry is one of the most dynamic developing sectors of the world economy and an important factor in the social and economic development of regions and countries. It is known as one of the three first-class profitable industries in the world, which in order to expand and develop requires the cooperation and agreement of environmental, cultural, economic, political and social factors at the community level, in this regard, the phenomenon what has now negatively affected the tourism industry in Iran and the world is the Corona (Covid-19) crisis. This study suggests an infrastructure for determining the factors affecting health tourism. Therefore, the newest multi-criteria decision-making method, fuzzy best-worst method was used to calculate the relative importance of indices and Fuzzy evaluation based on distance from average solution technique was applied as a multi-attribute decision-making method to rank effective factors in health tourism. The statistical population of this research consists of directors and experts in the tourism industry Mazandaran. Based on the results of this study, improving the level of tourist security in the province ranks first among other options for the economy itself.
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The study demonstrates the flexible functioning of the FITradeoff method that integrates the Holistic Evaluation with the Elicitation by Decomposition. For that purpose, the new features of the FITradeoff method in which integrates the two paradigms of preference modeling have been explored to solve a real multi-criteria decision problem. In this paper, a truck acquisition problem, at a midsize carrier faced with an uncertain and turbulent scenario due to the Coronavirus pandemic, was solved using the FITradeoff method. In this problem, seven criteria were considered to represent the Decision-Maker objectives. Also, six trucks (alternatives) have been examined by the Decision-Maker (Financial Director). The FITradeoff DSS supported the company as to obtain, through the combination of Holistic Evaluation and Elicitation by Decomposition, a ranking of all the trucks based on the preferences expressed during the decision process to ensure lower costs and higher profits in the long run, also guaranteeing a quicker (more efficient) resolution of the problem. © 2022, Springer Nature Switzerland AG.
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Purpose>This study aims to develop a risk prioritization process using failure mode and effect analysis (FMEA) in association with composition of probabilistic preferences (CPP) and weighting the risk analysis criteria. It seeks to develop decision-making considering the fast response necessary to achieve project objectives in complex scenarios, such as the pandemic of COrona VIrus Disease 19 (COVID-19).Design/methodology/approach>After identifying the risks, the prioritization process was applied to a project in the oil and gas area, in which a focus group assessed these risks. This evaluation took place employing traditional FMEA, FMEA with CPP by axes considering four points of view and FMEA with CPP by weighted sum with the use of a multicriteria method to weight the criteria. These approaches were compared to understand their differences and benefits, with a flow chart being developed, consolidating the procedure.Findings>The methodologies that showed the greatest benefits were FMEA with CPP by axes PO (progressive-optimistic) and by weighted sum. Essentially, this was mainly related to the interrelationship between risks and to the importance of prioritization.Originality/value>This procedure can consider company's views on what is critical and the interrelationship between risks. It provides a clear segmentation of what should and should not be prioritized. It was also developed in a practical case, showing a possible alternative to support fast responses in decision-making.
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Renewable energy plays an important role in energy sustainability and environmental friendliness, for example in terms of biomass power, and is essential for generating electric power, which has a significant impact on sustainable energy for the future. The objective of this research, therefore, is to propose a sustainable method of assessment for biomass power plant site selection for electric power supply for field hospitals during the COVID-19 pandemic in Thailand In this research, based upon overlay map layers as an empirical study, the pandemic, as well as environmental friendliness, are considered in Thailand The Fuzzy Analytic Hierarchy Process (FAHP) has been applied in order to determine the weighting of the criteria. Furthermore, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) has also been applied in order to rank alternative locations. The contribution of this research lies in the development of an approach that is flexible in terms of sustainable assessment, in terms of both subjective and objective evaluation measures, in order to reduce bias in the decision-making process, as well as to include natural disaster assessment in providing guidelines for the location selection of a biomass power plant from the perspective of environment friendliness.