ABSTRACT
Multi-Criteria Decision Making (MCDM) methods help researchers in solving many prob-lems in terms of numerical analysis. However, MCDM methods have not been very popular in the health sector. In this study, five ones of Turkey's most intense and highly populated cities were selected and the risk of the spread of Covid-19 disease was evaluated on the basis of seven criteria. The PROMETHEE and the ELECTRE methods were conducted to rank the cities in terms of the spread of Covid-19. The PROMETHEE method correctly ranked the most risky city as Istanbul, but ELECTRE ranked Istanbul the second most risky. The results of the meth-ods are compared with real data. PROMETHEE gave more convenient results than ELECTRE. Also, this paper offers a new field of study to the literature.Cite this article as: Pekel ozmen E, Demir B. The analysis of risk assessment for the trans-mission of COVID-19 by using PROMETHEE and ELECTRE methods. Sigma J Eng Nat Sci 2023;41:2:232-242.
ABSTRACT
This paper presents a group decision-making mechanism to properly manage ranking problems in an intuitionistic fuzzy environment. TOPSIS ranking multi-criteria decision-making (MCDM) methods is utilized under the intuitionistic fuzzy set theory. This solution technique examines the sets of criteria employed in decision-making problems, the preferences of a group of decision-makers, and the importance levels of decision-makers. Managers use the ranking methods as a reliable technique for making supplier evaluation decisions. Furthermore, the supply chain suffers from the shortage of materials, transportation problems, etc. In the post COVID-19 era, the need for a practical and exhaustive tool is explicit. An illustrative case on a supplier selection problem considering sustainability and risks in the post-COVID-19 era is used to demonstrate the applicability of the proposed technique by detailing the procedure step by step. A comparative analysis of the results is carried out. The results are compared with the results of the MARCOS method. The results show that the presented methodology is applicable to the other areas as well. (English) [ FROM AUTHOR] Bu makale, sezgisel bulanık bir ortamda sıralama problemlerini düzgün bir şekilde yönetmek için bir grup karar verme mekanizması sunmaktadır. Sezgisel bulanık küme teorisi kapsamında çok kriterli karar verme (ÇKKV) yöntemi olan TOPSIS kullanılmaktadır. Bu çözüm tekniğinde karar verme problemlerinde kullanılan birtakım kriterler, karar vericiler grubunun tercihleri ve karar vericilerin önem düzeyleri incelenmektedir. Yöneticiler, sıralama yöntemlerini tedarikçi değerlendirme kararlarını vermek için güvenilir bir teknik olarak kullanır. Ayrıca, COVID-19 döneminden sonra tedarik zinciri malzeme sıkıntısı, ulaşım sorunları vb. sıkıntılardan muzdariptir, pratik ve kapsamlı bir araca olan ihtiyaç açıktır. Prosedürü adım adım detaylandırarak önerilen tekniğin uygulanabilirliğini göstermek için, COVID-19 sonrası dönemde sürdürülebilirliği ve riskleri dikkate alan bir tedarikçi seçimi sorununa ilişkin örnek bir vaka kullanılmıştır. Sonuçların karşılaştırmalı analizi gerçekleştirilmiştir. Sonuçlar, MARCOS yönteminin sonuçları ile karşılaştırılmıştır. Sonuçlar, sunulan metodolojinin diğer alanlara da uygulanabilir olduğunu göstermektedir. (Turkish) [ FROM AUTHOR] Copyright of Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Pamukkale University, Social Sciences Institute 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.)
ABSTRACT
The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts' original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts' weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis.
ABSTRACT
After the detection of the first official case diagnosed with COVID-19 on March 2020, the new package called as "Economic Stability Shield” have been implemented in Turkey to minimize the effects of the pandemic. As in all sectors, the epidemic caused various changes in the real estate sector. The demands of citizens who stayed in homes for a long time during the epidemic period have also changed when buying a house. This study aims to investigate the importance of selection criteria for buying a house criterion in the real estate sector and to determine the most suitable house of via a Multi-Criteria Decision Making (MCDM) approach. By integrating Fuzzy Analytic Hierarchy Process (FAHP) and Multi-Attribute Ideal Real Comparative Analysis (FMAIRCA), the assessment criteria of buying house and suitable houses are evaluated. While making this evaluation, the weights of the criteria are obtained by pairwise comparisons under fuzzy environment through collected from five households living in Konya. Next, the FMAIRCA method is used to rank the candidate houses according to the views of the households. As a result of the study, it has been determined that the criteria of eligibility for credit are the most important criteria. For the case study, we perform a comparative analysis on the performance of fuzzy TOPSIS and fuzzy VIKOR in buying a house. Our comparative analysis for this case study shows that the three fuzzy MCDM methods achieve the identical rankings. © 2023 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.
ABSTRACT
The emergence of Covid-19 has led to change within hospital-based healthcare. An example, has been to reconfigure clinical decision making meetings from traditional in-person (Face-to-face, FtF) to online video-conferencing (VC) format inorder to decrease contagion risk. Despite its widespread uptake, there is minimal empirical data evaluating this format. This narrative review considers the implications on medical decision-making when clinicians communicate remotely via Microsoft Teams. The discussion is informed by the psychological literature and by commentary obtained from a survey of paediatric cardiac clinicians who participated in clinical meetings when video-conferencing was first introduced. Whist video-conferencing can optimize clinician presence, this is potentially offset by compromises in current imaging quality, the group discussion, information sharing and decision quality. Implementing a shift from face-to-face to VC within the group decision-making process requires an appreciation of the changed environment, appropriate adaptations and the implemention of new technology solutions. Meanwhile, healthcare should carefully consider the potential implications of clinical decision making using online video conferencing, be prepared to adapt and evaluate prior to a shift away from face-to-face formats.
Subject(s)
COVID-19 , Cardiology , Humans , Child , Decision Making , Delivery of Health CareABSTRACT
The expansion of the online food delivery apps (OFDAs) around the globe has accelerated because of the sudden growing cases of the COVID-19 pandemic. OFDAs are quickly expanding in India, providing a huge number of chances for different OFDA platforms and creating a competitive market. There are several criteria and dimensions for OFDAs businesses to explore to keep with the frequently changing competitive market and achieve long-term success. A Pythagorean fuzzy set (PFS) is a powerful tool for dealing with uncertainty. Distance measure of PFS is a hot research topic and has real-life applications in many areas, such as decision making, medical diagnosis, patterns analysis, clustering, etc. The article aims to examine the results of the novel Pythagorean fuzzy distance measure strategy to select the best online app using TOPSIS method to select the best OFDAs. Firstly, all the axioms related to distance measures are proved for the proposed measures. The proposed work uses five distinct alternatives/options and four attributes/criteria in a fuzzy environment to deal with imprecise and conflicting information. The findings indicate that the proposed methodology is a more realistic way to choose the best OFDAs among others. Finally, a sensitivity analysis is used to determine whether the chosen alternative was the best option among the other components and to ensure that the TOPSIS technique results were accurate.
ABSTRACT
In an increasingly complex and uncertain decision-making environment, large-scale group decision-making (LSGDM) can offer a more efficient method, allowing a large number of decision-makers (DMs) to truly participate in the decision-making process. The consensus-reaching process (CRP) is an effective method for resolving conflicting opinions among large-scale DMs. However, in the existing CRP of LSGDM, the new consensus state and the adjustment cost borne by inconsistent DMs after implementing feedback suggestions are not taken into consideration. To address this issue, this paper proposes a global optimization feedback model with particle swarm optimization (PSO) for LSGDM in hesitant fuzzy linguistic environments. An improved density-based spatial clustering of applications with noise (DBSCAN) on hesitant fuzzy linguistic term sets (HFLTSs) is introduced to classify large-scale DMs into several clusters, and a weight determination method that combines cluster size and intra-cluster tightness is also presented. The consensus degree of clusters is calculated at two levels: intra-consensus and inter-consensus. To improve the global consensus level with minimum cost, a global optimization feedback model is established to generate recommendation advice for inconsistent DMs, and the model is solved by PSO. A numerical example related to "COVID-19” and some comparisons are provided to verify the feasibility and advantages of the proposed method.
ABSTRACT
A novel method for assessing the effectiveness of enrichment evaluations PROMETHEE combining pentagonal intuitionistic fuzzy numbers (PIFNs) and preference rank-ing organization is presented in the present paper. PIFN suggests a new technique for multi -criteria group decision making (MCGDM) in which two characteristic values of membership and non-membership functions are involved. The key practicality of incorporating PIFN in decision -making is its effective capability of managing the vagueness and uncertainties of linguistic terms used during discussions. The designed algorithm is then applied to get an appropriate, cost-effective, and publicly accepted awareness campaign to be used to forewarn populaces about any virulent disease, which has not been studied before. Importantly, it is the only way to protect any huge population of a country from any fatal disease, i.e. to be timely aware of the disease's transmissibility, severity, and precautionary measures through any effectively ap-proachable source. Here, we consider alternative sources of campaigns, such as commercial advertisement on television, on social media, on bills /other government circulars, billboards, and door-to-door volunteering for guidance. These alternative campaigns are based on five generalized criteria, where the weight of each criterion is evaluated via the fuzzy analytical hier-archy process (F-AHP). After using the F-AHP for complex decisions based on acceptance and effectiveness, the F-PROMETHEE algorithm is applied to achieve the closest ideal alternative.
ABSTRACT
Aiming at the multi-attribute group decision-making problem with interval-valued probabilistic uncertain linguistic term set (IVPULTS) and unknown expert weights, this paper proposed a decision-making method combining distance and similarity. Firstly, it employed the interval dominance degree method to rank interval-valued probabilities to form an ordered IVPULTS since the disorder of elements in IVPULTS causes the existing distance measure and decision-making result to be non-unique. At the same time, it expanded the existing distance by using the interval linguistic term distance measuring method considering the poor discriminative power. Secondly, based on the dual relationship between distance and similarity measures, this paper defined the distance similarity formula and determined the weights of different experts using the improved similarity-trust network analysis method. Next, it designed a TOPSIS decision-making method based on improved distance and similarity-trust network (IDSTN-TOPSIS) to obtain a unique and stable ranking of the alternatives. Finally, taking the selection of resilient suppliers of a medical supplies manufacturing company under COVID-19 as an example, experimental results verify the effectiveness and superiority of the proposed method. (English) [ FROM AUTHOR] 针对属性值为区间值概率不确定语言术语集（interval-valued probabilistic uncertain linguistic term set, IVPULTS）、专家权重未知的多属性群决策问题，提出一种融合距离和相似度的决策方法。首先，由于现有的 IVPULTS中元素的无序性导致距离测度及决策结果不唯一，利用区间优势度方法对区间值概率进行排序，从而 形成有序的IVPULTS；同时考虑到现有距离测度区分能力不高，利用不确定语言距离度量方法扩充现有距离公 式。其次，基于距离与相似测度存在的对偶关系，为IVPULTS定义了距离相似度公式，并利用改进的相似—信 任网络分析法确定不同专家的权重。再次，设计了基于改进距离和相似—信任网络的TOPSIS决策方法（improved distance and similarity-trust network TOPSIS，IDSTN-TOPSIS），从而得到唯一且稳定的方案排序。最后，以新 冠疫情下某医疗用品制造公司熔喷布弹性供应商选择为例，验证了所提方法的有效性和优越性。 (Chinese) [ FROM AUTHOR] Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition 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.)
ABSTRACT
The outbreak of the novel coronavirus pneumonia and the turbulent international situation in recent years have seriously disrupted the normal operation of the entire supply chain (SC). As an emerging technology, blockchain is characterized by decentralization, reliability, transparency and traceability, which can be effectively applied to solve social, environmental and economic concerns and achieve sustainability of supply chain. However, whether blockchain is suitable for every function of a sustainable supply chain (SSC), or what function is best suited for the application of a set of blockchain criteria, can be viewed as a multi-criteria group decision-making (MCGDM) problem. This paper presents a combined MCGDM technique utilizing the social network analysis (SNA) and Multi-Attributive Border Approximation Area Comparison (MABAC), for selecting an appropriate function of SSCs to implement blockchain technology with Neutrosophic information. The framework gives quantitative consideration to the weight of relevant blockchain criteria and decision makers under high uncertainty. This study can also facilitate the effective allocation of resources and enhance the competitiveness of SSCs in the coordinated planning of various blockchain deployments. © 2022 IEEE.
ABSTRACT
The massive spread of COVID-19 and the crash of China Eastern Airlines MU5735 have negatively impacted the public's perception of civil aviation safety, which further affects the progress of the civil aviation industry and economic growth. The aim of research is to investigate the public's perception of China's civil aviation safety and give the authorities corresponding suggestions. First, we use online comment collection and sentiment analysis techniques to construct a novel evaluation index system reflecting the public's greatest concern for civil aviation safety. Then, we propose two novel large-scale group decision-making (LSGDM) models for aggregating evaluation: (1) K-means clustering with a novel distance measure for evaluators combined with unsupervised K-means clustering in two-stage, (2) unsupervised K-means clustering for evaluators combined with unsupervised K-means clustering for processing evaluation in two-stage. Finally, we compare the characteristics of different models and use the average of the two models as the final evaluation results. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.
ABSTRACT
At a time of global epidemic control, the location of the medical logistics distribution center (MLDC) has an important impact on the operation of the entire logistics system to reduce the operating costs of the company, enhance the service quality and effectively control the COVID-19 on the premise of increasing the company's profits. Thus, the research on the location of MLDC has important theoretical and practical application significance separately. Recently, the TODIM and VIKOR method has been used to solve multiple-attribute group decision-making (MAGDM) issues. The probabilistic uncertain linguistic term sets (PULTSs) are used as a tool for characterizing uncertain information. In this paper, we design the TODIM-VIKOR model to solve the MAGDM in PULT condition. Firstly, some basic concept of PULTSs is reviewed, and TODIM and VIKOR method are introduced. The extended TODIM-VIKOR model is proposed to tackle MAGDM problems under the PULTSs. At last, a numerical case study for medical logistics center site selection (MLCSS) is given to validate the proposed method.
ABSTRACT
Leader selection plays a key role in how human social groups are formed and maintained. Leadership is either assigned through formal processes within an organization, or emerges informally through interactions with other group members-particularly in novel contexts. COVID-19 has accelerated the adoption of virtual meetings and more flexible team structures. However our understanding of how assigned leadership influences subsequent leadership emergence in virtual settings is limited. Here we examine the relationship between assigned leadership within an existing organization and subsequent emergent leadership attributions as members engage in virtual interactions. To do so, we created and implemented a novel virtual group decision-making task designed to support quantification of a more comprehensive set of communication style elements, such as speech dynamics and facial expressions, as well as task behaviors. Sixteen members of a real world organization engaged four repeated rounds of a group decision making task with new team members each time. We found participants made novel attributions of emergent leadership rather than relying solely on existing assigned leadership. While assigned leadership did influence leadership attributions, communication style, including amount of speech but also variability in facial expressions, played a larger role. The behavior of these novel emergent leaders was also more consistent with expectations of leadership behavior: they spoke earlier, more often, and focused more on the correct decision than did assigned leaders. These findings suggest that, even within existing social networks, virtual contexts promote flexible group structures that depend more on communication style and task performance than assigned leadership.
ABSTRACT
Multiplicative probabilistic linguistic preference relation (MPLPR) has been widely used by decision-makers (DMs) to tackle group decision-making (GDM) problems. However, due to the complexity of the decision-making circumstance and individual subjectivity of DMs, they often provide inconsistent MPLPRs which often lead to unreasonable decision results. To solve this problem, this paper investigates a novel approach to GDM with MPLPRs based on consistency improvement and upgraded multiplicative data envelopment analysis (DEA) cross-efficiency. First, the concept of sequential consistency of MPLPR is defined. Then, a consistency improvement algorithm is proposed, which can convert any unacceptable consistent MPLPR into an acceptable one. Furthermore, we use geometric averages to transform MPLPR into multiplicative preference relation (MPR). Meanwhile, considering the conservative psychology of DMs, an upgraded multiplicative DEA cross-efficiency model based on the pessimistic criterion is constructed, which can derive the priority vector of MPLPR. Therefore, we can obtain the rational ranking results for all alternatives. Finally, a case analysis of emergency logistics under COVID-19 is provided to illustrate the validity and applicability of the proposed approach. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press 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.)
ABSTRACT
In consideration of the different importance degrees that may be assigned to all possible linguistic terms, this paper investigates a novel three-way group decision-making method based on the probabilistic linguistic term set (PLTS) information systems. We first construct PLTS information systems based on multiple attributes. Considering the reliabilities of the experts, we determine the weights of the experts by the similarities of the information provided by the expert with regard to other experts. Subsequently, using the evidential reasoning (ER) method, we aggregate the information provided by all experts and obtain the conditional probability of each object. The introduction of the ER rules and the weights of experts successfully solve the problem of conflict between the evaluation information. Then an approach is presented to calculate loss functions and thresholds, which reduces the subjectivity of the decision-making process. Next, the decision result of each object is deduced based on the minimum-loss principle. Finally, a case study about the selection of mask foundries during the COVID-19 is used to demonstrate the effectiveness of our proposed method. And the superiority of our proposed method are proved by comparative analysis. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.
ABSTRACT
Large-scale group decision making (LSGDM) involving a large number of experts has attracted more and more scholars' attention. Many LSGDM methods assumed that experts were independent to make evaluations, but the development of social media promotes the communication among experts, which makes experts no longer independent. In addition, existing LSGDM methods mainly adopted aggregation strategies such as the weighted average operator and arithmetic average operator to integrate the opinion of experts in a cluster, which makes the aggregation results cannot reflect the real opinion of the expert group. To address these issues, considering the empathetic network of experts, this study proposes an LSGDM method based on a new aggregation method for expert space information. Firstly, we determine objective weights of experts according to the objective empathetic relationships among experts. Then, the Steiner-Weber point problem is used as a prototype to establish an aggregation method called the spatial optimal aggregation (SOA) method to fuse the spatial information of experts. The model is solved by the genetic algorithm. Finally, an illustrative example about the selection of the most urgent risk in the transportation of COVID-19 vaccines is presented to show the validity and practicability of the proposed model. © 2023 Elsevier Inc.
ABSTRACT
Reports an error in "An approach to MCGDM based on multi-granulation Pythagorean fuzzy rough set over two universes and its application to medical decision problem" by Bingzhen Sun, Sirong Tong, Weimin Ma, Ting Wang and Chao Jiang (Artificial Intelligence Review, 2022[Mar], Vol 55[3], 1887-1913). In the original article, the third and fourth author's affiliation were published incorrectly and the correct affiliations are given in this correction. (The following abstract of the original article appeared in record 2021-74641-001). Exploring efficiency approaches to solve the problems of decision making under uncertainty is a mainstream direction. This article explores the rough approximation of the uncertainty information with Pythagorean fuzzy information on multi-granularity space over two universes combined with grey relational analysis. Based on grey relational analysis, we present a new approach to calculate the relative degree or the attribute weight with Pythagorean fuzzy set and give a new descriptions for membership degree and non-membership. Then, this paper proposes a multi-granulation rough sets combined with Pythagorean fuzzy set, including optimistic multi-granulation Pythagorean fuzzy rough set, pessimistic multi-granulation Pythagorean fuzzy rough set and variable precision Pythagorean fuzzy rough set. Several basic properties for the established models are investigated in detail. Meanwhile, we present an approach to solving the multiple-criteria group decision making problems with fuzzy information based on the proposed model. Eventually, a case study of psychological evaluation of health care workers in COVID-19 show the principle of the established model and is utilized to verify the availability. The main contributions have three aspects. The first contribution of an approach of calculating the attribute weight is presented based on Grey Relational Analysis and gives a new perspective for the Pythagorean fuzzy set. Then, this paper proposes a mutli-granulation rough set model with Pythagorean fuzzy set over two universes. Finally, we apply the proposed model to solving the psychological evaluation problems. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
This paper presents a novel and comprehensive classification mechanism that groups numerous dimensions associated with group decision-making approaches. We identify three broad categorizations of group decision-making literature. The classified dimensions are clustered by whether they are intrinsic to the group of participants, the nature of the addressed problems (topics), or a choice in the decision process design. We highlight the unique challenges facing manual group decision-making and those linked with intelligent support for group decision-making. Manual rules governing traditional face-to-face meetings must be adapted to suit virtual meetings where co-decisions must be made electronically. Virtual group decision-making for large groups facilitated by the extensive use of social media tools is an emergent area that has proven to be a necessity that has had a tremendous influence on society for the whole world during the recent Covid-19 pandemic. We first present the associated challenges, followed by the potential solution of computer-based group decision-making systems. We further discuss binary and multi-features of group decision approaches with justifications and examples. Communication has been identified as the main bottleneck, and various attacks have been revealed. This research opens up several aspects of group decision-making that could be further studied. For example, a cluster of dimensions concerns the truthfulness of the information exchanged and how untruthfulness is handled, starting with detection and how to react to detected lies. The scale of technology-supported group decision-making has grown to the point where its influence has been accused or lauded in the last few US elections. There is a persistent call for properties such as transparency and fairness in group decision-making systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
To offer better treatment for a COVID-19 patient, preferable medicine selection has become a challenging task for most of the medical practitioners as there is no such proven information regarding it. This article proposes a decision-making approach for preferable medicine selection using picture fuzzy set (PFS), Dempster-Shafer (D-S) theory of evidence and grey relational analysis (GRA). PFS is an extended version of the intuitionistic fuzzy set, where in addition to membership and non-membership grade, neutral and refusal membership grades are used to solve uncertain real-life problems more efficiently. Hence, we attempt to use it in this article to solve the mentioned problem. Previously, researchers considered the neutral membership grade of the PFS similar to the other two membership values (positive and negative) as applied to the decision-making method. In this study, we explore that neutral membership grade can be associated with probabilistic uncertainty which is measured using D-S theory of evidence and FUSH operation is applied for the aggregation purpose. Then GRA is used to measure the performance among the set of parameters which are in conflict and contradiction with each other. In this process, we propose an alternative group decision-making approach by the evidence of the neutral membership grade which is measured by the D-S theory and the conflict and contradiction among the criteria are managed by GRA. Finally, the proposed approach is demonstrated to solve the COVID-19 medicine selection problem.
ABSTRACT
Recently, the major environmental change and a pandemic called COVID-19 have heavily impacted the economy, business, and health of each country. Moreover, the climatic changes and COVID-19 are calamities to human life. In other words, these two aspects threaten the existence of humans and the sustenance of the overall development of a country. These two factors particularly influence the tourism sector, so a strategy balancing environmental quality and dealing with the ill effects of COVID-19 is formulated to uplift the economic sectors. Atannasov's intuitionistic fuzzy domain is used to model the environmental quality and COVID-19 due to the involvement of hesitancy and uncertainty. The precise measurement of the imprecision in the information is obtained with the help of entropy measure. The paper analyzes the two aspects using a novel entropy measure based on multiple criteria sorting (MCS). Here, the two MCS problems are solved with the help of two proposed techniques: TOPSIS-GREY-sort and ENTROPY-TOPSIS-GREY-sort. A case study showing the impact of COVID-19 in the Philippines and the environmental quality of Tehran (the capital city of Iran) are considered to validate the functioning of the proposed techniques. We use "A novel sorting method TOPSIS-SORT: an application for Tehran environmental quality evaluation (2016), Ekonomica a management, " and "Current Issues in Tourism 25.2(2022): 168-178, Taylor and Francis " for the comparative analysis.