Development of a Multi-Criteria Model for Sustainable Reorganization of a Healthcare System in an Emergency Situation Caused by the COVID-19 Pandemic

Towards Green and Sustainable Healthcare: A Literature Review and Research Agenda for Green Leadership in the Healthcare Sector

The health sector is one of the keys to sustainable development. Although it is directly related to only one Sustainable Development Goal (Goal 3, "Ensuring a healthy life and promoting well-being at all ages"), the sector itself, which aims to protect health, is paradoxically at the same time the main emitter of environmental pollutants that have a negative impact on health itself. Therefore, sustainability has become a key priority for health sector organizations, and leadership in this area is essential at all levels. Scientific research plays a particular role here, helping to more clearly define the links between environmental sustainability and the health effects of a polluted environment and climate change as well as indicating the direction of actions needed and disseminating good practices that can help accelerate the adoption of efforts towards climate neutrality and sustainable development of health sector organizations. The aim of this article is to present the current state of the art and future research scenarios in the field of green and sustainable healthcare through a literature review by using the Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) method to perform a bibliometric analysis of papers published in 2012-2022. The Web of Science Core Collection (WoSCC) database is used for this purpose. A total of 144 papers are included for analysis, categorized based on eight fields: author(s), title, year of publication, country, journal, scientific category, and number of citations. Based on the results, themes for future research on green leadership in the healthcare sector are identified and recommended.

A Cross-Sectional Study on Mental Health of School Students during the COVID-19 Pandemic in India

The broad objective of the present study is to assess the levels of anxiety and depression of school students during the COVID-19 lockdown phase and their association with students’ background, stress, concerns and social support. In this regard, the present study follows a novel two stage approach. In the first phase, an empirical survey was carried out, based on multivariate statistical analysis, wherein a group of 273 school students participated in the study voluntarily. In the second phase, a novel Picture Fuzzy FFA (PF-FFA) method was applied for understanding the dynamics of facilitating and prohibiting factors for three categories of focus groups (FG), formulated on the basis of attendance in online classes. Findings revealed a significant impact of anxiety and depression on mental health. Further, PF-FFA examinedthe impact of the driving forces that steered children to attend class as contrasted to the the impact of the restricting forces.

Designing a fuzzy decision support framework for assessing solid waste management in the South European region

The environmental planning of cities and rural regions is associated with monitoring the performance of several services, including solid waste management. This study proposes a new framework for the comparative assessment of the performance of integrated municipal solid waste management. The framework includes the fuzzy MACBETH multi-criteria decision-making model used to investigate the uncertainties and inefficiencies associated with solid waste management systems. The model consists of all major stages in solid waste management and its environmental impact. The applicability of the model was examined in the South European region. According to the global fuzzy values of the criteria weights, the most influential reported criteria were GHG emission (0.113,0.157,0.202), the waste generation growth rate (0.034,0.063,0.141), and waste generation (0.034,0.054,0.127). The other results indicated that Italy (47.26) and France (42.67) had shown a better performance, only to be followed by Spain (37.68), whereas Greece (15.77) and Portugal (12.85) had received the lowest score. In the context of promoting the circular economy in Europe, having a higher recycling rate and less landfilling is beneficial for Greece and Portugal. In addition to this, all these countries should make efforts on decoupling the waste generation-GDP correlation. Furthermore, the applicability of the model depends on an appropriate scale and criteria. The model can be replicated to other developed societies with a few modifications. However, it is necessary to modify the criteria for assessing developing societies based on local conditions.

Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature

Considering the coronavirus disease 2019 (COVID-19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision-making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID-19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID-19 by presenting a systematic literature review of the state-of-the-art COVID-19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID-19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

A Multi-Criteria Decision Support and Application to the Evaluation of the Fourth Wave of COVID-19 Pandemic

The COVID-19 pandemic caused important health and societal damage across the world in 2020–2022. Its study represents a tremendous challenge for the scientific community. The correct evaluation and analysis of the situation can lead to the elaboration of the most efficient strategies and policies to control and mitigate its propagation. The paper proposes a Multi-Criteria Decision Support (MCDS) based on the combination of three methods: the Group Analytic Hierarchy Process (GAHP), which is a subjective group weighting method; Extended Entropy Weighting Method (EEWM), which is an objective weighting method; and the COmplex PRoportional ASsessment (COPRAS), which is a multi-criteria method. The COPRAS uses the combined weights calculated by the GAHP and EEWM. The sum normalization (SN) is considered for COPRAS and EEWM. An extended entropy is proposed in EEWM. The MCDS is implemented for the development of a complex COVID-19 indicator called COVIND, which includes several countries’ COVID-19 indicators, over a fourth COVID-19 wave, for a group of European countries. Based on these indicators, a ranking of the countries is obtained. An analysis of the obtained rankings is realized by the variation of two parameters: a parameter that describes the combination of weights obtained with EEWM and GAHP and the parameter of extended entropy function. A correlation analysis between the new indicator and the general country indicators is performed. The MCDS provides policy makers with a decision support able to synthesize the available information on the fourth wave of the COVID-19 pandemic.

Adapting Urban Transport Planning to the COVID-19 Pandemic: An Integrated Fermatean Fuzzy Model

The critical worldwide problem of adapting urban transport planning to COVID-19 is for the first time comprehensively addressed and solved in this study. It primarily aims to help transport planners increase the resilience of transport systems. Firstly, a multi-level decision-making hierarchy structure based on four main criteria and 17 sub-criteria is introduced for relevant stakeholders to provide a practical framework for assessing existing transport plans. Then, a three-stage integrated Fermatean fuzzy model for adapting urban transport planning to the pandemic is presented. The model hybridizes the method based on the removal effects of criteria (MEREC) and combined compromise solution (CoCoSo) method into a unique methodological framework under the Fermatean fuzzy environment. A case study provides decision-making guidelines on how to adapt transport plans to COVID-19 in the real-world context of Belgrade, Serbia. The research findings show that the pandemic significantly changed the priorities of transport planning strategies and measures. "Non-motorized travel" is now the best alternative since its numerous short-term measures lead to better transport service. The major advantages of the introduced model are higher flexibility and a more precise fusion of experts' preference information. The integrated Fermatean fuzzy model could be used for adapting other emerging problems to COVID-19.

Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review

The influence of the ongoing COVID-19 pandemic that is being felt in all spheres of our lives and has a remarkable effect on global health care delivery occurs amongst the ongoing global health crisis of patients and the required services. From the time of the first detection of infection amongst the public, researchers investigated various applications in the fight against the COVID-19 outbreak and outlined the crucial roles of different research areas in this unprecedented battle. In the context of existing studies in the literature surrounding COVID-19, related to medical treatment decisions, the dimensions of context addressed in previous multidisciplinary studies reveal the lack of appropriate decision mechanisms during the COVID-19 outbreak. Multiple criteria decision making (MCDM) has been applied widely in our daily lives in various ways with numerous successful stories to help analyse complex decisions and provide an accurate decision process. The rise of MCDM in combating COVID-19 from a theoretical perspective view needs further investigation to meet the important characteristic points that match integrating MCDM and COVID-19. To this end, a comprehensive review and an analysis of these multidisciplinary fields, carried out by different MCDM theories concerning COVID19 in complex case studies, are provided. Research directions on exploring the potentials of MCDM and enhancing its capabilities and power through two directions (i.e. development and evaluation) in COVID-19 are thoroughly discussed. In addition, Bibliometrics has been analysed, visualization and interpretation based on the evaluation and development category using R-tool involves; annual scientific production, country scientific production, Wordcloud, factor analysis in bibliographic, and country collaboration map. Furthermore, 8 characteristic points that go through the analysis based on new tables of information are highlighted and discussed to cover several important facts and percentages associated with standardising the evaluation criteria, MCDM theory in ranking alternatives and weighting criteria, operators used with the MCDM methods, normalisation types for the data used, MCDM theory contexts, selected experts ways, validation scheme for effective MCDM theory and the challenges of MCDM theory used in COVID-19 studies. Accordingly, a recommended MCDM theory solution is presented through three distinct phases as a future direction in COVID19 studies. Key phases of this methodology include the Fuzzy Delphi method for unifying criteria and establishing importance level, Fuzzy weighted Zero Inconsistency for weighting to mitigate the shortcomings of the previous weighting techniques and the MCDM approach by the name Fuzzy Decision by Opinion Score method for prioritising alternatives and providing a unique ranking solution. This study will provide MCDM researchers and the wider community an overview of the current status of MCDM evaluation and development methods and motivate researchers in harnessing MCDM potentials in tackling an accurate decision for different fields against COVID-19.

Comparison of the RAFSI and PIV method in multi-criteria decision making: application to turning processes

Multi-criteria decision-making (MCDM) methods are used in many fields so as to rank alternatives and find the best one. However, rank reversal after adding or removing an alternative can occur in using some of the methods. In this study, two methods RAFSI and PIV were compared for application of making multi-criteria decisions. They are known to be capable of avoiding rank reversal problems. Sixteen 9XC steel turning tests were performed for the experiment. Tool holder length, spindle speed, feed rate and depth of cut are parameters that vary in each test. Three criteria for evaluating the turning process consist of MRR, RE and Ra. Four methods including MEREC, ROC, RS and EQUAL were used for determining weights of the criteria. The blend of two multi-criteria decision making methods (RAFSI and PIV) with four weight-determining methods resulted in eight ranking options. This is a new approach of the study. A positive outcome was reached that all eight ranking options identified the same best test. The best experiment must ensure to have maximum MRR and minimum RE and Ra simultaneously. A detailed discussion of the ranking results in each case was also carried out. Finally, the directions and issues that need to be studied further were pointed out in this paper as well.

Circular intuitionistic fuzzy TOPSIS method: pandemic hospital location selection

A circular intuitionistic fuzzy set (CIFS) recently introduced by Atanassov as a new extension of intuitionistic fuzzy sets is represented by a circle whose radius is r and whose center is composed of membership and non-membership degrees. The idea is similar to type-2 fuzzy sets, which are based on the fuzziness of membership functions with a third dimension. CIFSs help us define membership functions more flexibly, taking into account the vagueness in membership and non-membership degrees. In this study, TOPSIS, which is a multi-criteria decision-making (MCDM) method, is developed under circular intuitionistic fuzzy environment. The proposed CIF-TOPSIS method is applied to determine the most appropriate pandemic hospital location selection problem. Then, a sensitivity analysis based on criteria weights and the weight of the decision maker’s optimistic and pessimistic attitudes are conducted to check the robustness of the decisions given by the proposed approach. A comparative analysis with the single-valued intuitionistic fuzzy TOPSIS, Pythagorean fuzzy TOPSIS, picture fuzzy TOPSIS methods is also performed to verify the developed approach and to demonstrate its effectiveness.

Simulation-based multi-criteria decision making: an interactive method with a case study on infectious disease epidemics

Whenever a system needs to be operated by a central decision making authority in the presence of two or more conflicting goals, methods from multi-criteria decision making can help to resolve the trade-offs between these goals. In this work, we devise an interactive simulation-based methodology for planning and deciding in complex dynamic systems subject to multiple objectives and parameter uncertainty. The outline intermittently employs simulation models and global sensitivity analysis methods in order to facilitate the acquisition of system-related knowledge throughout the iterations. Moreover, the decision maker participates in the decision making process by interactively adjusting control variables and system parameters according to a guiding analysis question posed for each iteration. As a result, the overall decision making process is backed up by sensitivity analysis results providing increased confidence in terms of reliability of considered decision alternatives. Using the efficiency concept of Pareto optimality and the sensitivity analysis method of Sobol' sensitivity indices, the methodology is then instantiated in a case study on planning and deciding in an infectious disease epidemic situation similar to the 2020 coronavirus pandemic. Results show that the presented simulation-based methodology is capable of successfully addressing issues such as system dynamics, parameter uncertainty, and multi-criteria decision making. Hence, it represents a viable tool for supporting decision makers in situations characterized by time dynamics, uncertainty, and multiple objectives.

COVID ‐19 critical success factors in Indian healthcare industry—A DEMATEL approach

The prosed study aims to provide COVID‐19 critical success factors (CSF) associated with pandemic circumstances in the Indian healthcare industry (HCI). The CSF was identified via expert team inputs and a detailed literature review. The Decision‐Making Trial and Evaluation Laboratory (DEMATEL) method is used to determine the causal relationship between identified CSF. The methodology was supported by the case study of the Indian HCI. A total of 15 CSF in the Indian HCI during COVID‐19 are identified and prioritized using the DEMATEL method. The findings indicate that the high‐quality personal protective equipment (PPEs; LC8) and testing laboratories/facilities, centres, and kits (LC15) are the significant cause, and appropriate healthcare laws (LC13) are the least effect group. The study shows that policy and decision‐makers need to emphasize on LC8 and LC15 CSF in the Indian HCI and act accordingly to win the battle against post‐COVID‐19 circumstance. The policy/decision‐makers and healthcare administrations can identify the CSF and focus on that particular CSF. The identified CSF will help policy and decision‐makers swiftly build up the HCI to cope with the future pandemic.

Performance of Fuzzy Multi-Criteria Decision Analysis of Emergency System in COVID-19 Pandemic. An Extensive Narrative Review

The actual coronavirus disease 2019 (COVID-19) pandemic has led to the limit of emergency systems worldwide, leading to the collapse of health systems, police, first responders, as well as other areas. Various ways of dealing with this world crisis have been proposed from many aspects, with fuzzy multi-criteria decision analysis being a method that can be applied to a wide range of emergency systems and professional groups, aiming to confront several associated issues and challenges. The purpose of this critical review was to discuss the basic principles, present current applications during the first pandemic wave, and propose future implications of this methodology. For this purpose, both primary sources, such as scientific articles, and secondary ones, such as bibliographic indexes, web pages, and databases, were used. The main search engines were PubMed, SciELO, and Google Scholar. The method was a systematic literature review of the available literature regarding the performance of the fuzzy multi-criteria decision analysis of emergency systems in the COVID-19 pandemic. The results of this study highlight the importance of the fuzzy multi-criteria decision analysis method as a beneficial tool for healthcare workers and first responders’ emergency professionals to face this pandemic as well as to manage the created uncertainty and its related risks.