A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system

This research attempts to study the Supplier Selection and Order Allocation Problem (SSOAP) considering three crucial concepts, namely responsiveness, sustainability, and resilience. To do so, the current research develops a Multi-Stage Decision-Making Framework (MSDMF) to select potential suppliers and determine the quantity of orders. The first stage aims at computing the scores of the suppliers based on several indicators. To do this, a novel decision-making approach named the Stochastic Fuzzy Best–Worst Method (SFBWM) is developed. Then, in the second stage, a Multi-Objective Model (MOM) is suggested to deal with supplier selection and order allocation decisions. In the next step, a data-driven Fuzzy Robust Stochastic (FRS) optimization approach, based on the fuzzy robust stochastic method and the Seasonal Autoregressive Integrated Moving Average (SARIMA) methods, is employed to efficiently treat the hybrid uncertainty of the problem. Afterwards, a novel solution method named the developed Chebyshev Multi-Choice Goal Programming with Utility Function (CMCGP-UF) is developed to obtain the optimal solution. Moreover, given the crucial role of the Medical Equipment (ME) industry in society's health, especially during the recent Coronavirus disease, this important industry is taken into account. The outcomes of the first stage demonstrate that agility, cost, GHG emission, quality, robustness, and Waste Management (WM), respectively, are the most important criteria. The outcomes of the second stage determine the selected suppliers, utilized transportation systems, and established sites. It is also revealed that demand directly affects all the objective functions while increasing the rate of disruptions has a negative effect on the sustainability measures.

Implementation of Clean Hospital Strategy and Prioritizing Covid-19 Prevention Factors Using Best-Worst Method.

Many nations have suffered the catastrophe of COVID-19, and one of the first countries affected by the pandemic was Iran; all industries and individuals have been adversely affected by the pandemic. Health care systems and patients' conditions, in particular, were disrupted due to canceling elective surgery. To put it more sharply, a delay in performing elective surgery may potentially impact patients' survival and the quality of their lives. To cope with the new situation, in the first stage, the Clean Hospital strategy was proposed in order to minimize the effects of this pandemic on elective surgical services. The mentioned strategy is a try to provide a solution and resume elective surgeries in the pandemic period. In the second stage, panel discussion, Delphi method, and the best-worst method (BWM) were employed to prioritize the factors that inhibit Coronavirus transmission. The proposed strategy and the results of this study could be used by policymakers and health departments to resume elective surgeries and control the infection to maintain a hospital or a section of it clear. The overall result of the study showed that the most important Covid-19 prevention factors in Clean Hospitals were personal protection (w = 0.212), screening checklist (w = 0.182), and check body temperature (w = 0.126), respectively (C1 > C2 > C3). According to the financial, time, and human resource limitations, first, resources were allocated to higher priority criteria, and in order of priority, all items (C1, C2, …., C9) were used in the Clean hospital strategy.

A State-of the-Art Survey of Best-Worst Method Applications for the Problems Related to COVID-19

The best-worst method (BWM) and its variations have been widely utilized in decision making problems since 2015. During the COVID-19 pandemic, several problems occurred considering the decision making related to legislative, health system, precautions, transportation, and economic decisions. The aim of this paper is to analyze the state-of the-art survey of BWM applications for the problem related to COVID-19. To do so, a bibliographic analysis of literature is conducted. "COVID-19" and "best-worst method" are searched in the Scopus database as keywords, and 40 studies out of 47 studies are taken into consideration. Keywords and s of these studies are analyzed with the N-grams approach and VOSviewer software. In addition, a descriptive analysis is done focusing on the authors' information and journals. The descriptive analysis demonstrates the topics in the retrieved studies comprehend COVID-19 and BWM with sustainable development goals, healthcare technology, supply chain, risk management, energy consumption, and logistics topics. The descriptive analysis part reveals that supply chain, service quality, social sustainability, supplier selection, sustainable development, green supply, and energy usage are the main topics in the considered literature.

A LAG-based framework to overcome the challenges of the sustainable vaccine supply chain: an integrated BWM–MARCOS approach

PurposeEach individual needs to be vaccinated to control the spread of the COVID-19 pandemic in the shortest possible time. However, the vaccine distribution with an already strained supply chain in low- and middle-income countries (LMICs) will not be effective enough to vaccinate all the population in stipulated time. The purpose of this paper is to show that there is a need to revolutionize the vaccine supply chain (VSC) by overcoming the challenges of sustainable vaccine distribution.Design/methodology/approachAn integrated lean, agile and green (LAG) framework is proposed to overcome the challenges of the sustainable vaccine supply chain (SVSC). A hybrid best worst method (BWM)–Measurement of Alternatives and Ranking According to COmpromise Solution (MARCOS) methodology is designed to analyze the challenges and solutions.FindingsThe analysis shows that vaccine wastage is the most critical challenge for SVSC, and the coordination among stakeholders is the most significant solution followed by effective management support.Social implicationsThe result of the analysis can help the health care organizations (HCOs) to manage the VSC. The effective vaccination in stipulated time will help control the further spread of the virus, which will result in the normalcy of business and availability of livelihood for millions of people.Originality/valueTo the best of the author's knowledge, this is the first study to explore sustainability in VSC by considering the environmental and social impact of vaccination. The LAG-based framework is also a new approach in VSC to find the solution for existing challenges.

Supply chain resilience strategies and their impact on sustainability: an investigation from the automobile sector

PurposeThe purpose of this study is to propose a framework comprising supply chain (SC) resilience strategies to handle low-frequency high impact disruptive events. This study also evaluates the impact of SC resilience strategies' implementation on the triple bottom line of SC sustainability.Design/methodology/approachA hybrid three-phased method is proposed to meet the research objectives of this study. In the first phase, this study uses the Delphi technique to select SC resilience strategies and SC sustainability dimensions. In the second phase, the best–worst method is used to assess the relative weights of resilience strategies. Finally, in the third stage, summative Likert scoring is used to understand the impact of SC resilience strategies on the SC sustainability triple bottom line.FindingsThe outcomes reveal that firms give due importance to inter-organizational relationships and supplier nearness for supply continuity. In the sustainability context, the obtained scores proved that resilience strategies have the maximum impact on economic sustainability, followed by environmental sustainability.Research limitations/implicationsTo the best of the authors' knowledge, this is the first study that examines aspects of SC resilience strategies and quantifies their impact on the triple bottom line of SC sustainability. This study is specific to the automobile sector;sectoral diversity may expose similarities and dissimilarities in the approach.Practical implicationsThe outcome establishes that supplier–manufacturer relationships need to be strengthened further to tackle any future uncertainties. Besides, supplier location decisions may also be revisited. The strategies proposed will aid SC managers to make informed decisions to prepare for uncertain events.Originality/valueIn the face of uncertain events, often SCs trade-off sustainability in pursuit of resilience. It manifests that resilience is a prerequisite for SC sustainability. While planning SCs, organizations often choose either sustainability or resilience. Thus, this study acknowledges the need to develop effective SC resilience strategies that are in harmony with the sustainability agenda.

Circular economy practices in the leather products industry toward waste valorization: an approach of sustainable environmental management

Purpose: The global resolution of embracing dynamic and intertwined production systems has made it necessary to adopt viable systems like circular economy (CE) to ensure excellency in the business. However, in emerging countries, it is challenging to implement the CE practices due to the existing problems in the supply chain network, as well as due to the vulnerable financial condition of the business after the deadly hit of COVID-19. The main aim of this research is to determine the barriers to implementing CE considering the recent pandemic and suggest strategies to organizations to ensure CE for a cleaner environment and greener economy. Design/methodology/approach: After an extensive literature review and validation from experts, 24 sub-barriers under the class of 6 main barriers are finalized by Pareto analysis, which is further analyzed via the best-worst method to determine the weight and rank of the barriers Further, fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank the proposed startegies to overcome the analysed barriers. Findings: The results identified "unavailability of initial funding capital”, "need long time investment”, "lack of integrating production system using advance technology” and "lack of strategic planning” as the most acute sub-barriers to CE implementation. Further, fuzzy TOPSIS method is used to suggest the best strategy to mitigate the ranked barriers. The results indicated "integrated design facility to CE”, "ensuring large scale funding for CE facility” as the best strategy. Practical implications: This study will motivate managers to implement CE practices to enjoy proper utilization of the resources, sustainable benefits in business, and gain competitive advantage. Originality/value: Periodically, a lot of work is done on CE practices but none of them highlighted the issues in the domain of the leather products industry (LPI) and COVID-19 toward achieving sustainability in production and consumption. Thus, some significant barriers and strategies to implement CE for achieving sustainability in LPI are highlighted in this study, which is a unique contribution to the literature. © 2023, Emerald Publishing Limited.

Data-driven product ranking: A hybrid ranking approach

The sudden COVID-19 epidemic has caused consumers to gradually switch to online shopping, the increasing number of online consumer reviews (OCR) on Web 2.0 sites has made it difficult for consumers and merchants to make decisions by analyzing OCR. Much of the current literature on ranking products based on OCR ignores neutral reviews in OCR, evaluates mostly given criteria and ignores consumers' own purchasing preferences, or ranks based on star ratings alone. This study aims to propose a new decision support framework for the evaluation and selection of alternative products based on OCR. The decision support framework mainly includes three parts: 1) Data preprocessing: using Python to capture online consumer comments for data cleaning and preprocessing, and extracting key features as evaluation criteria;2) Sentiment analysis: using Naive Bayes to analyze the sentiment of OCR, and using intuitionistic fuzzy sets to describe the emotion score;3) Benchmark analysis: a new IFMBWM-DEA model considering the preference of decision makers is proposed to calculate the efficiency score of alternative schemes and rank them according to the efficiency score. Then, the OCR of 15 laptops crawled from JD.com platform is used to prove the usefulness and applicability of the proposed decision support framework in two aspects: on the one hand, the comparison of whether the preference of decision makers is considered, and on the other hand, the comparison with the existing ranking methods. The comparison also proves that the proposed method is more realistic, the recommendations are more scientific and the complexity of the decision is reduced. [ 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.)

Operational challenges during a pandemic: an investigation in the electronics industry

PurposeThe recent coronavirus disease 2019 (COVID-19) pandemic poses numerous challenges to supply chains. This pandemic is quite unique when compared to previous epidemic disruptions and has had a severe impact on supply chains. As a result, the operational challenges (OCs) caused by COVID-19 are still unknown among practitioners and academics. It is critical to comprehensively document current OCs so that firms can plan and implement strategies to overcome them. Consequently, this study systematically identifies and ranks COVID-19-related OCs.Design/methodology/approachThis study uses an integrated methodology combining expert interviews and the best-worst method (BWM) to analyze the results. The data have been collected from the electronics industry of Bangladesh, an emerging economy. This study also conducts a sensitivity analysis to check the robustness of the results.FindingsThe results reveal 23 COVID-19-related OCs under five categories: sourcing, production and inventory management, demand management and distribution, return management and after-sales service, and supply chain-wide challenges. The quantitative investigation reveals that overstock in finished goods inventory, low end-customer demands, order cancellations from dealers and retailers, high inventory holding costs and lack of transportation are the top five OCs.Practical implicationsThe findings will help practitioners to understand the OCs and allow them to prepare for future major disruptions and formulate long-term strategies for operations during and after the COVID-19 pandemic.Originality/valueThis study contributes to the literature on supply chain complexity and challenges by considering a major pandemic outbreak. Moreover, the study also contributes to the knowledge on emerging economies, which have been largely neglected in the current literature.

An integrated interval type-2 fuzzy rough technique for emergency decision making

This paper aims to develop a hybrid emergency decision-making (EDM) method by combining best–worst method (BWM), multi-attributive border approximation area comparison (MABAC) and prospect theory (PT) in trapezoidal interval type-2 fuzzy rough (TrIT2FR) environment. In this hybrid method, the decision information is represented by trapezoidal interval type-2 fuzzy rough numbers (TrIT2FRNs). Firstly, this paper defines the TrIT2FRN and analyzes its desirable properties. Then, the TrIT2FR-BWM is developed to determine criteria weights. To develop the TrIT2FR-BWM, this paper completes the following three core issues: (i) propose an effective theorem to normalize the TrIT2FR weights;(ii) build a crisp programming model to transform the minmax objective of weight-determining model for the TrIT2FR-BWM;(iii) design a consistency ratio for the TrIT2FR-BWM to check the reliability of the determined criteria weights. Afterwards, this paper extends the classical MABAC into TrIT2FR environment to calculate the border approximation area (BAA). Subsequently, the PT is used to rank the alternatives, in which the calculated BAA is selected as the reference point. Lastly, the validity of the proposed method is certificated with a real site selection case of makeshift hospitals on COVID-19. Sensitivity analysis and comparative analyses are conducted to illustrate the robustness and superiorities of the proposed method. Some valuable results are summarized as follows: (i) the best alternative determined by the proposed method conforms with the actual selection result, (ii) the proposed models in the TrIT2FR-BWM have strong robustness, (iii) PT is helpful to improve the decision quality of EDM. © 2023 Elsevier B.V.

An integrated multi-criteria decision-making approach for identifying the risk level of musculoskeletal disorders among handheld device users.

In work-from-home (WFH) situation due to coronavirus (COVID-19) pandemic, the handheld device (HHD) users work in awkward postures for longer hours because of unavailability of ergonomically designed workstations. This problem results in different type of musculoskeletal disorders (MSDs) among the HHD users. An integrated multi-criteria decision-making approach was offered for identifying the risk level of MSDs among HHD users. A case example implemented the proposed approach in which, firstly, the best-worst method (BWM) technique was used to prioritize and determine the relative importance (weightage) of the risk factors. The weightages of the risk factors further used to rank the seven alternatives (HHD users) using Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) technique. The outcomes of the BWM investigation showed that the three most significant risk factors responsible for MSDs are duration of working, poor working posture and un-ergonomic design. The outcome of the VIKOR technique exhibited that computer professionals were at the highest risk among all users. The risk factor priority must be used for designing a working strategy for the WFH situation which will help to mitigate the risks of MSDs.

A model for green-resilient supplier selection: fuzzy best-worst multi-criteria decision-making method and its applications.

Supplier selection is regarded as the primary goal of supply chain management (SCM) because it affects its performance, productivity, pleasure, flexibility, and system speed in lockdown. A new method is proposed based on a multi-stage fuzzy sustainable supplier index (FSSI). Experts can use the triple bottom line (TBL) criteria to select the best supplier. In addition, the worst method is proposed based on trapezoidal membership and fuzzy membership functions, which can cover uncertainties and ambiguous environments. Because it collects the related criteria and sub-criteria and uses a direct fuzzy methodology, this research has impacted the SCM literature because it helps solve previous expert methods' computational difficulties. In addition, an ordered mean integration representation method has been implemented to prioritize the selection of the best supplier (SS) based on the sustainability performance of the best supplier, which improves the selection accuracy compared to the previous ranking method. This study can be used as a benchmark to determine which supplier is the best in sustainability. To provide the superiority and broad applicability of the proposed model, a practical case study was completed. On the other hand, the COVID-19 pandemic harms productivity, company performance, and selecting the best suppliers based on sustainability performance. The lockdown situation caused by the COVID-19 pandemic hurts company performance and management.

Modelling supply chain sustainability challenges in the food processing sector amid the COVID-19 outbreak.

The recent COVID-19 pandemic has significantly impacted most businesses and their supply chains. Due to the negative impacts of COVID-19, businesses have been facing numerous challenges. Among them, sustainability challenges are critical for any supply chain. In the literature, several studies have discussed the impacts of the COVID-19 pandemic on supply chains; however, there is a significant research gap in analysing supply chain sustainability challenges amid the COVID-19 outbreak in a particular context. To fill this research gap, this study aims to develop a systematic approach to identifying and analysing COVID-19 outbreak-related supply chain sustainability challenges in the context of the Australian food processing sector. To achieve the aims, this paper develops a mixed-method approach consisting of both qualitative and quantitative techniques, namely online survey and the Best-Worst method. From the online survey among experts from the Australian food processing sector, 22 sustainability challenges were finalised and categorised into four categories, namely, economic, environmental, social and ethical, and operational challenges. The empirical findings from the exploratory investigation reveal that increased food processing cost, lack of transparency and traceability, increase in price of raw materials, lack of capital and physical resources, and spread of fake information are the top five sustainability challenges to the Australian food processing sector due to the impacts of the COVID-19 outbreak. The findings of this study will help decision-makers, practitioners, and policymakers by developing the policies, guidelines, and strategies to overcome the most impactful sustainability challenges to ensure sustainable recovery from the impacts of the COVID-19 outbreak.

Analyzing factors contributing to COVID-19 mortality in the United States using artificial intelligence techniques.

Having started since late 2019, COVID-19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID-19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best-Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best-Worst Method-local linear neuro-fuzzy-adaptive neuro-fuzzy inference system approach, the relationship between COVID-19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.

A goal programming-based fuzzy best-worst method for the viable supplier selection problem: a case study.

Since the COVID-19 outbreak has led to drastic changes in the business environment, researchers attempt to introduce new approaches to improve the capability and flexibility of the industries. In this regard, recently, the concept of the viable supply chain, which tried to incorporate the leagile, resiliency, sustainability, and digitalization aspects into the post-pandemic supply chain, has been introduced by researchers. However, the literature shows that there is lack of study that investigated the viable supplier selection problem, as one of the crucial branches of viable supply chain management. Therefore, to cover this gap, the current work aims to develop a decision-making framework to investigated the viable supplier selection problem. In this regard, owing to the crucial role of the oxygen concentrator device during the COVID-19 outbreak, this research selects the mentioned product as a case study. After determining the indicators and alternatives of the research problem, a novel method named goal programming-based fuzzy best-worst method (GP-FBWM) is proposed to compute the indicators' weights. Then, the potential alternatives are prioritized employing the Fuzzy Vlse Kriterijumsk Optimizacija Kompromisno Resenje method. In general, the main contributions and novelties of the present research are to incorporate the elements of the viability concepts in the supplier selection problem for the medical devices industry and to develop an efficient method GP-FBWM to measure the importance of the criteria. Then, the developed method is implemented and the obtained results are analyzed. Finally, managerial and theoretical implications are provided. Supplementary Information: The online version contains supplementary material available at 10.1007/s00500-022-07572-0.

How, When, & Where Temporary Hospitals Fit in Turbulent Times: A Hybrid MADM Optimization in the Middle East.

Governments have been challenged to provide temporary hospitals and other types of facilities to face the COVID-19 pandemic. This research proposes a novel multi-attribute decision-making (MADM) model to help determine how, when, and where these temporary facilities should be installed based on a set of critical success factors (CSFs) mapped in an uncertain environment. We portray the available facilities for temporary hospitals based on the CSFs that must be considered to make critical decisions regarding the optimal position based on the government's strategic decision-making process, thus indirectly providing better services and maximizing resources. In relation to earlier work, this research builds upon hybrid Pythagorean fuzzy numbers to find weights in Best-Worst Methods and rank temporary facilities based on evaluation by an area-based method for ranking. Policy implications and future directions are derived.

Industry 5.0 challenges for post-pandemic supply chain sustainability in an emerging economy

To ensure business continuity in the post-COVID-19 era, decision-makers should reconfigure their traditional supply chain (SC) networks, assisted by the development and implementation of cutting-edge technologies. Recently, Industry 5.0 (I5.0) has gained increasing attention as a paradigm offering salient features for the creation of resilient and inclusive operations by ensuring long-standing SC sustainability. However, there has been insufficient analysis of the challenges of implementing I5.0 in SCs. Therefore, this study aims to understand the challenges of implementing I5.0 when managing the impact of SC disruptions due to the COVID-19 pandemic in an emerging economy. Both qualitative and quantitative methods were used for this research. First, the challenges to I5.0 implementation were identified through a literature review and experts' feedback. Those challenges were examined and prioritised using the Best-Worst Method (BWM). Second, the contextual relationships amongst these challenges were analysed using interpretive structural modelling (ISM) with cross-impact matrix multiplication applied to classification (MICMAC) analysis. Findings showed that to adopt I5.0 initiatives successfully in order to manage the post-COVID-19 impact on SC sustainability, the active involvement of senior managers is required in the execution process. Findings also showed that financial support and funding (e.g., tax reduction, short loans, etc.) from investors and the government play a pivotal role in enabling sustainability in SCs. Finally, the challenges were classified using MICMAC analysis to provide valuable insights for managing future disruptions. This study is expected to help managers and decision-makers successfully overcome the challenges of implementing I5.0 in SCs and thus improve SC sustainability.

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.

COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model.

The fight against the COVID-19 pandemic, which has affected the whole world in recent years and has had devastating effects on all segments of society, has been one of the most important priorities. The Turkish Standards Institution has determined a checklist to contribute to developing safe and clean environments in higher education institutions in Turkey and to follow-up on infection control measures. However, this study is only a checklist that makes it necessary for decision-makers to make a subjective evaluation during the evaluation process, while the need to develop a more effective, systematic framework that takes into account the importance levels of multiple criteria has emerged. Therefore, this study applies the best-worst method under interval type-2 fuzzy set concept (IT2F-BWM) to determine the importance levels of criteria affecting the "COVID-19 safe campus" evaluation of universities in the context of global pandemic. A three-level hierarchy consisting of three main criteria, 11 sub-criteria, and 58 sub-criteria has been created for this aim. Considering the hierarchy, the most important sub-criterion was determined as periodic disinfection. The high contribution of the interval-valued type-2 fuzzy sets in expressing the uncertainty in the decision-makers' evaluations and the fact that BWM provides criterion weights with a mathematical optimization model that produces less pairwise comparisons and higher consistency are the main factors in choosing this approach. Simple additive weighting (SAW) has also been injected into the IT2F-BWM to determine the safety level of any university campus regarding COVID-19. Thus, decision-makers will be better prepared for the devastating effects of the pandemic by first improving the factors that are relatively important in the fight against the pandemic. In addition, a threshold value will be determined by considering all criteria, and it will prepare the ground for a road map for campuses. A case study is employed to apply the proposed model, and a comparison study is also presented with the Bayesian BWM to validate the results of the criteria weights.

Identifying and prioritizing resilient health system units to tackle the COVID-19 pandemic.

Since human health greatly depends on a healthy and risk-free social environment, it is very important to have a concept to focus on improving epidemiology capacity and potential along with economic perspectives as a very influential factor in the future of societies. Through responsible behavior during an epidemic crisis, the health system units can be utilized as a suitable platform for sustainable development. This study employs the Best-Worst Method (BWM) in order to develop a system for identifying and ranking health system units with understanding the nature of the epidemic to help the World Health Organization (WHO) in recognizing the capabilities of resilient health system units. The purpose of this study is to identify and prioritize the resilient health system units for dealing with Coronavirus. The statistical population includes 215 health system units in the world and the opinions of twenty medical experts are also utilized as an informative sample to localize the conceptual model of the study and answer the research questionnaires. The resilient health system units of the world are identified and prioritized based on the statistics of "Total Cases", "Total Recovered", "Total Deaths", "Active Cases", "Serious", "Total Tests" and "Day of Infection". The present descriptive cross-sectional study is conducted on Worldometer data of COVID-19 during the period of 17 July 2020 at 8:33 GMT. According to the results, the factors of "Total Cases", "Total Deaths", "Serious", "Active Cases", "Total Recovered", "Total Tests" and "Day of Infection" are among the most effective ones, respectively, in order to have a successful and optimal performance during a crisis. The attention of health system units to the identified important factors can improve the performance of epidemiology system. The WHO should pay more attention to low-resilience health system units in terms of promoting the health culture in crisis management of common viruses. Considering the importance of providing health services as well as their significant effect on the efficiency of the world health system, especially in critical situations, resilience analysis with the possibility of comparison and ranking can be an important step to continuously improve the performance of health system units.

Modeling the artificial intelligence-based imperatives of industry 5.0 towards resilient supply chains: A post-COVID-19 pandemic perspective.

The recent COVID-19 pandemic has significantly affected emerging economies' global supply chains (SCs) by disrupting their manufacturing activities. To ensure business survivability during the current and post-COVID-19 era, it is crucial to adopt artificial intelligence (AI) technologies to renovate traditional manufacturing activities. The fifth industrial revolution, Industry 5.0 (I5.0), and artificial intelligence (AI) offer the overwhelming potential to build an inclusive digital future by ensuring supply chain (SC) resiliency and sustainability. Accordingly, this research aims to identify, assess, and prioritize the AI-based imperatives of I5.0 to improve SC resiliency. An integrated and intelligent approach consisting of Pareto analysis, the Bayesian approach, and the Best-Worst Method (BWM) was developed to fulfill the objectives. Based on the literature review and expert opinions, nine AI-based imperatives were identified and analyzed using Bayesian-BWM to evaluate their potential applicability. The findings reveal that real-time tracking of SC activities using the Internet of Things (IoT) is the most crucial AI-based imperative to improving a manufacturing SC's survivability. The research insights can assist industry leaders, practitioners, and relevant stakeholders in dealing with the impacts of large-scale SC disruptions in the post-COVID-19 era.