A Hybrid Spherical Fuzzy MCDM Approach to Prioritize Governmental Intervention Strategies against the COVID-19 Pandemic: A Case Study from Vietnam

Digital twin and fuzzy framework for supply chain sustainability risk assessment and management in supplier selection

Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks.

Streamlining apartment provider evaluation: A spherical fuzzy multi-criteria decision-making model

In the context of the thriving real estate market in developing countries like Vietnam, understanding consumer preferences and effectively addressing them through a comprehensive multi-criteria decision-making (MCDM) framework is paramount for real estate providers. This study presents a two-stage MCDM model that integrates the Delphi technique and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on Spherical Fuzzy Sets (SFSs). Initially, the SF-Delphi technique validates critical criteria influencing customers' apartment selection in Vietnam. Secondly, the SF-TOPSIS method evaluates the top ten apartment providers. To ensure robustness and validity, a comparative analysis compares the results with those from the Intuitionistic Fuzzy TOPSIS (IF-TOPSIS) and Fuzzy TOPSIS (F-TOPSIS) methods. Subsequently, five rank correlation coefficients (Spearman, Kendall, Goodman-Kruskal, Weighted rank measure of correlation, Weighted Similarity) are used to assess the relationships between various TOPSIS techniques applied to apartment suppliers in Vietnam. The correlation coefficients demonstrate strong agreement among the TOPSIS methods, with the smallest coefficient being 0.7778, surpassing the threshold of 0.7. This high level of consistency confirms the efficacy of the proposed TOPSIS approach with different Fuzzy Sets in reliably evaluating customers' preferences for apartment suppliers. Notably, the legal aspect's prominence underscores its critical role in shaping customer choices, emphasizing the significance of considering legal factors in the context of apartment supply and demand in Vietnam. Furthermore, using SFSs makes this approach particularly suited to capture consumer perceptions within the dynamic and uncertain business environment characterized by volatility, uncertainty, complexity, and ambiguity (VUCA).

An interpreter ranking index-based MCDM technique for COVID-19 treatments under a bipolar fuzzy environment

The entire world is currently fighting the severe and dangerous pandemic COVID-19, which is causing bodily suffering and mental distress due to the rapidly increasing number of infected patients and deaths worldwide. Many COVID-19 treatments are going on in India, and some treatments are under development for these patients. But, treatment selection for the COVID-19 patients is challenging in the present situation. Through the multi-criteria decision-making technique, they can select the COVID-19 treatments easily. Therefore, we have developed an MCDM technique to select COVID-19 treatments in India. This paper invented the value and ambiguity of bipolar fuzzy (BF) numbers. Additionally, some fundamental theorems and properties of BF-numbers are studied. A novel positive and negative interpreter ranking index of BF numbers has been introduced. In the present day, most human decision-making relies heavily on bipolar fuzzy information. Hence, we developed an MCDM technique with bipolar fuzzy details. A comprehensive range of human decisions for selecting COVID-19 treatments is based on positive and negative double-sided or bipolar judgemental thinking. To select COVID-19 treatments in India, we have applied the proposed MCDM technique with BTrF information. Moreover, to demonstrate the applicability of our proposed MCDM method, we have considered a numerical example with BF data. Finally, we give the comparison study to show the effectiveness of our proposed MCDM method with other existing decision-making methods.

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic's main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID-19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.

A decision-making framework for COVID-19 infodemic management strategies evaluation in spherical fuzzy environment

100 years after the Spanish flu, the COVID-19 crisis showed that large-scale epidemics and pandemics do not belong to the past. On the report of the World Health Organization, COVID-19 is the most significant public health problem of the twenty-first century. Like previous epidemics, the current crisis is accompanied by uncertainty, mistrust, doubt and fear, and this has led to an infodemic connection to the epidemic. So not only are we fighting an epidemic, but also, we are brawling an infodemic. To reduce the social and economic consequences and harmful effects of infodemic health, and to overcome it, we need to implement strategies against infodemic. Evaluating strategies based on multiple characteristics can be considered multi-criteria decision-making (MCDM) problem. According to the literature, there is no study that aims on proposing an integrated approach to evaluate infodemic management strategies under uncertain environment. Therefore, in this paper, an integrated framework based on the extended version of best-worst method (BWM) and Combined Compromise Solution (CoCoSo) methods under a spherical fuzzy set (SFS) is developed for the first time to address the COVID-19 infodemic management strategies selection. Initially, the criteria are weighted using the developed SFS BWM which reduces uncertainty in pairwise comparisons. In the next step, the 15 selected strategies are analyzed and ranked using SFS CoCoSo. The outputs of this paper illustrate that online tools for fact checking COVID-19 information and engage and empower communities are placed in the first and second priorities, respectively. The comparison of ranking results SFS-CoCoSo with other MCDM methods demonstrates the performance of the proposed approach and its ranking stability.

Risk assessment approach for analyzing risk factors to overcome pandemic using interval-valued q-rung orthopair fuzzy decision making method

The process of developing and implementing sustainable strategies to prevent spread of COVID-19 for society typically requires integrating all social, technological, economic, governmental aspects in a systematic way. Since the clear understanding of risk factors contribute to the success of the strategies applied against COVID-19, a risk assessment procedure is applied in this study to properly evaluate risk factors cause to spread of pandemic as a multi-complex decision problem. Therefore, due to the evaluation of risk factors, which often involves uncertain information, the model is constructed based on interval-valued q-rung orthopair fuzzy-COmplex PRoportional ASsessment (IVq-ROF-COPRAS) method. While the developed framework is efficient to enhance the quality of decisions by implementing more realistic, precise, and effective application procedure under uncertain environment, it has capability to help governments for developing comprehensive strategies and responses. According to the results of the proposed risk analysis model, the top three risk factors are "The Approach that Prioritizes the Economy in Policies", "Insufficient Process Control in Normalization" and "Lack of Epidemic Management Culture in Individuals and Businesses". Lastly, to show applicability and efficiency of the model sensitivity and comparative analysis were conducted at the end of the study.

Intelligent model for contemporary supply chain barriers in manufacturing sectors under the impact of the COVID-19 pandemic

The COVID-19 pandemic has cast a shadow on the global economy. Since the beginning of 2020, the pandemic has contributed significantly to the global recession. In addition to the health damages of the pandemic, the economic impacts are also severe. The consequences of such effects have pushed global supply chains toward their breaking point. Industries have faced multiple obstacles, threatening the fragile flow of raw materials, spare parts, and consumer goods. Previous studies showed that supply chain barriers have multi-faceted impacts on industries and supply chains, which demand appropriate measures. In this regard, seven major barriers that directly impact industries have been identified to determine which industry is most affected by the COVID-19 pandemic. This paper utilized a hybrid multi-criteria decision-making (MCDM) approach under a neutrosophic environment using trapezoidal neutrosophic numbers to rank those barriers. The Analytical Network Process (ANP) quantifies the effects and considers the interrelationships between the determined barriers (criteria) involved in decision-making. Subsequently, the Measurement Alternatives and Ranking according to the COmpromise Solution (MARCOS) method was adopted to rank six industries according to the impact of those barriers. Results show that the lack of inventory is the largest barrier to influencing industries, followed by the lack of manpower. Sensitivity analysis is performed to detect the change in the rank of industries according to the change in the relative importance of the barriers.

A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown: A pilot study for İstanbul

COVID-19 pandemic has affected the entire world. During the Covid-19 pandemic, which is tried to be prevented by all countries of the world, regulations have been made to reduce the effect of the virus in sectors such as banking, tourism, and especially transportation. Social isolation is one of the most critical factors for people who have or are at risk of contracting COVID-19 disease. Many countries have developed different solutions to ensure social isolation. By applying lockdown for specific periods, preventing the movement of people will reduce the rate of transmission. However, some private and public institutions that have to serve during the lockdown period should be carefully determined. In this study, we aim to determine the petrol stations to serve during the COVID-19 lockdown, and this problem is handled as a multi-criteria decision-making problem. We extend the spherical fuzzy VlseKriterijumska Optimizacija IKompromisno Resenje (SF-VIKOR) method with the spherical fuzzy Analytic Hierarchy Process (SF-AHP). To show its applicability in complex decision-making problems, Istanbul is selected to perform a case study; thirteen petrol stations are evaluated as potential serving petrol station alternatives during the lockdown. Then, the novel SF-AHP integrated SF-VIKOR methodology is structured; the problem is solved with this methodology, and the best alternative is determined to serve in lockdown. Accessibility of the petrol station and Measures taken by station managers are determined to be essential for the effectiveness of the lockdown process. The neighborhood population and the station's proximity to hospitals are also critical inner factors to fight the pandemic. To test the methodology, Spherical Fuzzy the Weighted Aggregated Sum-Product Assessment (SF-WASPAS) is utilized. Public or private organizations can use the proposed methodology to improve their strategies and operations to prevent the spreading of COVID-19.

Social media and COVID-19 vaccination hesitancy: mediating role of the COVID-19 vaccine perception

Individuals' COVID-19 vaccination behaviors were examined when the government introduced a new vaccine into the immunization program. The purpose of this study is to thoroughly examine the effects of COVID-19 risk perception (CR), COVID-19 vaccination perception (VC), and Social Media (SO) on COVID-19 vaccine hesitancy (HE) in Vietnam. Three hundred fifty samples were collected regarding a reluctance to vaccinate against COVID-19 from 6/2021 to 7/2021. This is when immunizations are administered and injected in Vietnam; hence, hesitation regarding injection is rather prevalent. Multivariate regression analysis is conducted on a dataset of 350 Vietnamese respondents using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) approach. The main results indicated that the Perception Vaccine functions as a link between VC and HE. CR has a positive effect on both HE and VC; whereas VC has a negative impact on HE. Simultaneously, the study illustrates the detrimental effect of SO on immunity by comparing it to the influence of social media. The study's findings also demonstrated the critical role of protection motivational theory (PMT) and information theory in promoting vaccination efforts in Vietnam.

An integrated multi-criteria decision-making approach to optimize the number of leagile-sustainable suppliers in supply chains

Global supply chains are increasingly becoming complex by having numerous suppliers around the world. To manage this complexity, organizations must identify the optimum number of suppliers. There have been many examples in the literature that used different approaches to solve this problem. Despite the importance of this issue, less attention has been paid to it and managers of the companies do not know how, and based on which approach and criteria, they should determine the optimal number of suppliers which leads to lower cost and higher reliability of the production line. Therefore, in this study, a hybrid methodology is proposed to expose the process of this problem which helps managers to learn how they can determine the optimal number of suppliers. We address this gap by developing an integrated approach based on multi-criteria decision-making (MCDM) comprising best-worst method (BWM), simple additive weighting (SAW), and technique for order preference by similarity to ideal solution (TOPSIS), and simulation to determine the optimal number of suppliers. This study utilizes a comprehensive approach based on leagile and environmentally sustainable criteria to determine the optimal number of suppliers. To examine the efficiency of the proposed approach, an empirical case study is conducted in an Iranian oil company. The final results represent that the scenario with a 1-1-1 arrangement (one supplier for each type of equipment) is the best possible scenario to determine the optimal number of leagile-sustainable suppliers. To examine the reliability and robustness of the obtained results, a sensitivity analysis based on the three most important criteria is conducted. Finally, discussions on the findings as well as theoretical and managerial implications are presented.

Investigating the Effectiveness of Government Public Health Systems against COVID-19 by Hybrid MCDM Approaches

To elucidate the effectiveness of the containment strategies against the pandemic, a Multi-Criteria Decision Making (MCDM) model is established to evaluate the government’s performance against COVID-19. In this study, the Analytic Hierarchy Process (AHP), Entropy, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method are used in determining the performance of the public health system. We adopt both subjective and objective weighting methods for a more accurate evaluation. In addition, the evaluation of performance against COVID-19 is conducted in various aspects and divided into different periods. Data Envelopment Analysis (DEA) is applied to evaluate the sustainability of the public health system. Composite scores of the public health system are determined based on the performance and sustainability assessment. The five countries, South Korea, Japan, Germany, Australia, and China are rated with higher composite scores. On the country, the US, Indonesia, Egypt, South Africa, and Brazil receive lower rating scores among the countries for evaluation. This modeling study can provide a practical quantitative justification for developing containment policies and suggestions for improving the public health system in more countries or areas.

Multi-Criteria Decision-Making System for Wind Farm Site-Selection Using Geographic Information System (GIS): Case Study of Semnan Province, Iran

Selecting the best place for constructing a renewable power plant is a vital issue that can be considered a site-selection problem. Various factors are involved in selecting the best location for a renewable power plant. Therefore, it categorizes as a multi-criteria decision-making (MCDM) problem. In this study, the site selection of a wind power plant is investigated in a central province of Iran, Semnan. The main criteria for classifying various parts of the province were selected and pairwise compared using experts’ opinions in this field. Furthermore, multiple restrictions were applied according to local and constitutional rules and regulations. The Analytic Hierarchy Process (AHP) was used to weigh the criteria, and according to obtained weights, wind speed, and slope were the essential criteria. Moreover, a geographic information system (GIS) is used to apply the weighted criteria and restrictions. The province’s area is classified into nine classes according to the results. Based on the restrictions, 36.2% of the total area was unsuitable, mainly located in the north part of the province. Furthermore, 2.68% (2618 km2) and 4.98% (4857 km2) of the total area are the ninth and eightieth classes, respectively, which are the best locations for constructing a wind farm. The results show that, although the wind speed and slope are the most essential criteria, the distance from power facilities and communication routes has an extreme impact on the initial costs and final results. The results of this study are reliable and can help to develop the wind farm industry in the central part of Iran.

A Novel Integrating Data Envelopment Analysis and Spherical Fuzzy MCDM Approach for Sustainable Supplier Selection in Steel Industry

Supply chain sustainability, which takes environmental, economic, and social factors into account, was recently recognized as a critical component of the supply chain (SC) management evaluation process and known as a multi-criteria decision-making problem (MCDM) that is heavily influenced by the decision-makers. While some criteria can be analyzed numerically, a large number of qualitative criteria require expert review in linguistic terms. This study proposes an integration of Data Envelopment Analysis (DEA), spherical fuzzy analytic hierarchy process (SF-AHP), and spherical fuzzy weighted aggregated sum product assessment (SF-WASPAS) to identify a sustainable supplier for the steel manufacturing industry in Vietnam. In this study, both quantitative and qualitative factors are considered through a comprehensive literature review and expert interviews. The first step employs DEA to validate high-efficiency suppliers based on a variety of quantifiable criteria. The second step evaluates these suppliers further on qualitative criteria, such as economic, environmental, and social factors. The SF-AHP was applied to obtain the criteria’s significance, whereas the SF-WASPAS was adopted to identify sustainable suppliers. The sensitivity analysis and comparative results demonstrate that the decision framework is feasible and robust. The findings of this study can assist steel industry executives in resolving the macrolevel supplier selection problem. Moreover, the proposed method can assist managers in selecting and evaluating suppliers more successfully in other industries.

A Cross-Country European Efficiency Measurement of Maritime Transport: A Data Envelopment Analysis Approach

Maritime transport, which includes shipping and port operations, is the fundamental basis of international trade and globalization. In transportation management, efficiency is critical for verifying performance and proposing the best countermeasure to meet predetermined goals. Various efforts in this field have been made to solve this problem satisfactorily. However, the significant proportion of conventional approaches are based on long-term observations and professional expertise, with only a few exceptions based on practice-based historical data. Data Envelopment Analysis (DEA) is a non-parametric technique for analyzing various output and input variables parallelly. The efficiency of maritime transport in European countries is explored using a two-stage DEA approach based on Malmquist and Epsilon-Based Measure (EBM). First, the Malmquist model analyses countries’ total productivity growth rates and their breakdown into technical efficiency (catch-up) and technology change (frontier-shift). Second, the EBM model is used to determine the efficiency and inefficiency of the maritime transportation systems in each European country. Apart from identifying the best-performing countries in specific areas over the study period (2016–2019), the results highlight that the gap in applying the EBM method to maritime transport has been successfully closed and that the emerging paradigm, when combined with the Malmquist model, can be a sustainable and appropriate evaluation model for other research areas.

How to Achieve Passenger Satisfaction in the Airport? Findings from Regression Analysis and Necessary Condition Analysis Approaches through Online Airport Reviews

Delivering high-quality service to passengers can be critical for an airport’s survival, competitiveness, profitability, and long-term growth in a highly competitive environment. The present study aims to examine the relationship between airport service attributes and passenger satisfaction. To this end, we conducted multi-method research consisting of symmetric (multiple regression analysis—MRA) and asymmetric (necessary condition analysis—NCA) approaches. The research data consists of 1463 valid online reviews (n = 1463) of the top 50 busiest airports in Europe retrieved from Skytrax. The MRA was employed to examine the net effect of the eight airport service attributes on passenger satisfaction, while the NCA was used to explore the necessary conditions and level of necessity to achieve passenger satisfaction. Using MRA, the findings reveal that airport staff is the most influential predictor of passenger satisfaction, whereas airport shopping and airport Wi-Fi connectivity do not have a significant effect on passenger satisfaction. Moreover, the NCA results found that six of the eight conditions are necessary to achieve passenger satisfaction at the airport. To complement and comprehend the findings, this study also sheds light on the antecedents underlying airport passenger satisfaction in the post-COVID-19 era using NCA.

E-Learning Platform Assessment and Selection Using Two-Stage Multi-Criteria Decision-Making Approach with Grey Theory: A Case Study in Vietnam

Education has changed dramatically due to the severe global pandemic COVID-19, with the phenomenal growth of e-learning, whereby teaching is undertaken remotely and on digital platforms. E-learning is revolutionizing education systems, as it remains the only option during the ongoing crisis and has tremendous potential to fulfill instructional plans and safeguard students’ learning rights. The selection of e-learning platforms is a multi-criteria decision-making (MCDM) problem. Expert analyses over numerous criteria and alternatives are usually linguistic terms, which can be represented through grey numbers. This article proposes an integrated approach of grey analytic hierarchy process (G-AHP) and grey technique for order preference by similarity to ideal solution (G-TOPSIS) to evaluate the best e-learning website for network teaching. This introduced approach handles the linguistic evaluation of experts based on grey systems theory, estimates the relative importance of evaluation criteria with the G-AHP method, and acquires e-learning websites’ ranking utilizing G-TOPSIS. The applicability and superiority of the presented method are illustrated through a practical e-learning website selection case in Vietnam. From G-AHP analysis, educational level, price, right and understandable content, complete content, and up-to-date were found as the most impactful criteria. From G-TOPSIS, Edumall is the best platform. Comparisons are conducted with other MCDM methods; the priority orders of the best websites are similar, indicating the robust proposed methodology. The proposed integrated model in this study supports the stakeholders in selecting the most effective e-learning environments and could be a reference for further development of e-learning teaching-learning systems.

A Hybrid Model with Spherical Fuzzy-AHP, PLS-SEM and ANN to Predict Vaccination Intention against COVID-19

This study aims to identify the key factors affecting individuals’ behavioral vaccination intention against COVID-19 in Vietnam through an online questionnaire survey. Differing from previous studies, a novel three-staged approach combining Spherical Fuzzy Analytic Hierarchy Process (SF-AHP), Partial Least Squares-Structural Equation Model (PLS-SEM), and Artificial Neural Network (ANN) is proposed. Five factors associated with individuals’ behavioral vaccination intention (INT) based on 15 experts’ opinions are considered in SF-AHP analysis, including Perceived Severity of COVID-19 (PSC), Perceived COVID-19 vaccines (PVC), Trust in government intervention strategies (TRS), Social Influence (SOI), and Social media (SOM). First, the results of SF-AHP indicated that all proposed factors correlate with INT. Second, the data of 474 valid respondents were collected and analyzed using PLS-SEM. The PLS-SEM results reported that INT was directly influenced by PVC and TRS. In contrast, SOI had no direct effect on INT. Further, PSC and SOM moderated the relationship between PVC, TRS and INT, respectively. The ANN was deployed to validate the previous stages and found that the best predictors of COVID-19 vaccination intention were PVC, TRS, and SOM. These results were consistent with the SF-AHP and PLS-SEM models. This research provides an innovative new approach employing quantitative and qualitative techniques to understand individuals’ vaccination intention during the global pandemic. Furthermore, the proposed method can be used and expanded to assess the perceived efficacy of COVID-19 measures in other nations currently battling the COVID-19 outbreak.