Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: Pareto-based algorithms

A novel multi-objective robust possibilistic flexible programming to design a sustainable apparel closed-loop supply chain network

Designing a sustainable Closed-Loop Supply Chain (CLSC) network is imperative for the apparel industry, given its escalating adverse effects on economic, environmental, and social dimensions. In this study, a novel tri-objective location-allocation optimization model is specifically developed for designing a sustainable apparel CLSC, incorporating the industry's unique facilities. The aim of the model is to simultaneously minimize the costs and negative environmental impacts while maximizing social benefits under demands and returns uncertainty. A notable research contribution lies in addressing the unique challenges of treating different types of returns, including commercial, End Of Use (EOU) and End Of Life (EOL) returns due to their uncertain quality and quantity. Additionally, the model optimizes the environmental performance levels of production facilities, a novel aspect in the apparel CLSC research. Moreover, the flexibility of constraints related to the demand fulfilment is considered. To cope with such flexibility and uncertainties, a new hybrid robust possibilistic flexible programming model is developed, by extending the previous methodologies. A core innovation of this solution approach lies in the pioneering utilization of hexagonal fuzzy numbers for uncertain epistemic parameters, making a significant advancement in the field of CLSC. Comparative analysis with the similar studies demonstrates the superiority of the proposed model, incorporating hexagonal fuzzy numbers over the method using triangular fuzzy numbers. Furthermore, AUGMECON method using lexicographic optimization is applied to handle the multi-objective model. The application of the proposed model is shown focusing on Southwestern Ontario in Canada. The results reveal that commercial and EOU returns have a more detrimental impact on economic, environmental, and social sustainability aspects compared to EOL returns.

Predicting stock price of construction companies using weighted ensemble learning

Modeling the behavior of stock price data has always been one of the challenging applications of Artificial Intelligence (AI) and Machine Learning (ML) due to its high complexity and dependence on various conditions. Recent studies show that this will be difficult to do with just one learning model. The problem can be more complex for companies in the construction sector, due to the dependency of their behavior on more conditions. This study aims to provide a hybrid model for improving the accuracy of prediction for the stock price index of companies in the construction section. The contribution of this paper can be considered as follows: First, a combination of several prediction models is used to predict stock prices so that learning models can cover each other's errors. In this research, an ensemble model based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Classification and Regression Tree (CART) is presented for predicting the stock price index. Second, the optimization technique is used to determine the effect of each learning model on the prediction result. For this purpose, first, all three mentioned algorithms process the data simultaneously and perform the prediction operation. Then, using the Cuckoo Search (CS) algorithm, the output weight of each algorithm is determined as a coefficient. Finally, using the ensemble technique, these results are combined and the final output is generated through weighted averaging on optimal coefficients. The proposed system was implemented, and its efficiency was evaluated by real stock data of construction companies. The results showed that using CS optimization in the proposed ensemble system is highly effective in reducing prediction error. According to the results, the proposed system can predict the price index with an average accuracy of 96.6 %, which shows a reduction of at least 2.4 % in prediction error compared to the previous methods. Comparing the evaluation results of the proposed system with similar algorithms indicates that our model is more accurate and can be useful for predicting the stock price index in real-world scenarios.

A systematic review of modeling approaches in green supply chain optimization

Over the past decade, the significance of optimizing green supply chain management (GSCM) has gained unprecedented attention from both scholars and industry professionals. This surge in interest has led researchers to employ diverse modeling approaches in the pursuit of enhancing green supply chain networks. In this systematic review, we analyze 159 recent GSCM optimization papers published from 2017 to 2022 and identify the recent trends in mathematical modeling, multi-objective optimization, and the modeling/solver tools utilized. We find that the primary green focus is on minimizing carbon emissions (n = 44), reflecting the increasing concern for environmental sustainability. Among the modeling approaches employed, mixed-integer linear programming has emerged as the most popular choice (n = 51), followed by game theory-based modeling (n = 30). When it comes to multiobjective optimization, the ε-constraint approach is the most widely used. Evolutionary algorithms have emerged as the dominant meta-heuristic optimization approach. Additionally, the widely utilized solver in this domain is CPLEX with the most popular modeling/solver combination being GAMS/CPLEX. Moreover, the Journal of Cleaner Production was the leading outlet for research in this domain (n = 35). In addition to these findings, this study also discusses some other research trends and future research directions. Finally, we discuss the theoretical, managerial, and policy implications of this study. By providing GSCM researchers and practitioners with the latest trends in GSCM optimization approaches, this study contributes to the further advancement of the field.

Mapping and visualizing of research output on waste management and green technology: A bibliometric review of literature

The transition to a circular economy (CE) and environmental protection highly depends on waste management (WM) and green technology (GT). The purpose of this study is to examine the past two decades of WM and GT research to identify the most significant advancements and potential future research areas. Bibliometrics content analysis and text mining were utilized to resolve the subsequent issues: Has WM and GT research developed over time in the CE industry? Does WM and GT research have a clearly defined purpose? How do you foresee the future of WM and GT research in the context of CE evolving? Consequently, 1149 journal articles from the Scopus database were used to create and evaluate bibliometric networks. Therefore, five significant CE-related issues requiring additional research were identified: The first category is bio-based WM, followed by CE transition, GT, ecological impacts, municipal solid WM and lifecycle assessment, and finally, bio-based WM. Future research topics and a tool for the CE transition may be impacted by the investigation of inclusive WM systems, GT practices and their defining highlight patterns (which aim to minimalize waste generation). Future research goals include reducing waste and implementing WM into the CE framework.

Workforce scheduling efficiency assessment in construction projects through a multi-objective optimization model in the COVID-19 context

The COVID-19 disease has caused a drastic stoppage in the construction industry as a result of quarantines. For this reason, this study focuses on the workforce scheduling problem when working under COVID labor distancing constraints, and additional costs derived from deviation hours or hiring new employees that managers must assume on a project due to circumstances. A multi-objective mixed integer linear programming model was developed and solved using weighting and epsilon constraint methods to evaluate workforce scheduling and the mentioned COVID costs. The first objective function corresponds to the sum of the total extra hours; the second objective function represents the total non-worked but paid hours. Two sets of experiments are presented, the first based on a design of experiments that seeks to determine the relationship between the proposed objective functions and a methodology to determine the cost of considering COVID constraints. The second set of experiments was applied in a real company, where the situation without COVID vs with COVID, and without allowing extra hours vs with COVID allowing extra hours were compared. Obtained results showed that hiring additional employees to the man-crew leads the company to increase the extra hours cost up to 104.25%, being more convenient to keep a workforce baseline and to pay extra hours costs. Therefore, the mathematical model could represent a potential tool for decision-making in the construction sector, regarding the effects of COVID-19 costs on workforce scheduling construction projects. Consequently, this work contributes to the construction industry by quantifying the impact of COVID-19 constraints and the associated costs, offering a proactive approach to address the challenges posed by the COVID-19 pandemic for the construction sector.

Resilient and social health service network design to reduce the effect of COVID-19 outbreak

With the severe outbreak of the novel coronavirus (COVID-19), researchers are motivated to develop efficient methods to face related issues. The present study aims to design a resilient health system to offer medical services to COVID-19 patients and prevent further disease outbreaks by social distancing, resiliency, cost, and commuting distance as decisive factors. It incorporated three novel resiliency measures (i.e., health facility criticality, patient dissatisfaction level, and dispersion of suspicious people) to promote the designed health network against potential infectious disease threats. Also, it introduced a novel hybrid uncertainty programming to resolve a mixed degree of the inherent uncertainty in the multi-objective problem, and it adopted an interactive fuzzy approach to address it. The actual data obtained from a case study in Tehran province in Iran proved the strong performance of the presented model. The findings show that the optimum use of medical centers' potential and the corresponding decisions result in a more resilient health system and cost reduction. A further outbreak of the COVID-19 pandemic is also prevented by shortening the commuting distance for patients and avoiding the increasing congestion in the medical centers. Also, the managerial insights show that establishing and evenly distributing camps and quarantine stations within the community and designing an efficient network for patients with different symptoms result in the optimum use of the potential capacity of medical centers and a decrease in the rate of bed shortage in the hospitals. Another insight drawn is that an efficient allocation of the suspect and definite cases to the nearest screening and care centers makes it possible to prevent the disease carriers from commuting within the community and increase the coronavirus transmission rate.

The IoT-enabled sustainable reverse supply chain for COVID-19 Pandemic Wastes (CPW)

Supply chains have been impacted by the COVID-19 pandemic, which is the most recent worldwide disaster. After the world health organization recognized the latest phenomena as a pandemic, nations became incapacitated to provide the required medical supplies. In the current situation, the world seeks an essential solution for COVID-19 Pandemic Wastes (CPWs) by pushing the pandemic to a stable condition. In this study, the development of a supply chain network is contrived for CPWs utilizing optimization modeling tools. Also, an IoT platform is devised to enable the proposed model to retrieve real-time data from IoT devices and set them as the model's inputs. Moreover, sustainability aspects are appended to the proposed IoT-enabled model considering its triplet pillars as objective functions. A real case of Puebla city and 15 experiments are used to validate the model. Furthermore, a combination of metaheuristic algorithms utilized to solve the model and also seven evaluation indicators endorse the selection of efficient solution approaches. The evaluation indicators are appointed as the inputs of statistical and multicriteria decision-making hybridization to prioritize the algorithms. The result of the Entropy Weights method and Combined Compromise Solution approach confirms that MOGWO has better performance for the medium-sizes, case study and an overall view. Also, NSHHO outclasses the small-size and large-size experiments.

Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory

A sustainable environment by decreasing fossil fuel utilization and anthropogenic greenhouse gases is a globally main goal due to climate change and serious air pollution. Carbon dioxide (CO2) is a heat-trapping (greenhouse) that is released into the earth's atmosphere from natural processes, such as volcanic respiration and eruptions, as well as human activities, such as burning fossil fuels and deforestation. Due to this fact, underground carbon storage (UCS) is a promising technology to cut carbon emissions. However, there are some barriers to prevent UCS from applying globally. One of them is evaluating the feasibility of storage projects. Thus, the prediction accuracy of CO2 storage efficiencies may promote the attention of the community for UCS. In this study, we utilize the recent advances of swarm intelligence to develop a hybrid algorithm called AOSMA, employed to train the long short-term memory (LSTM). The developed swarm intelligence method (AOSMA) is an enhanced Aquila optimizer (AO) using the search mechanism of the slime mould algorithm (SMA). It is used to boost the prediction capability of the LSTM by optimizing its parameters. We considered two CO2 trapping indices, called residual trapping index (RTI) and solubility trapping index (STI). The evaluation experiments have shown that the AOSMA achieved significant results compared to the original AO and SMA and several swarm intelligence and optimization algorithms. The developed smart tools could use as a game changer to provide fast and accurate storage efficiency for projects that have similar parameters falling within the range of the database.

Identifying resilience strategies for disruption management in the healthcare supply chain during COVID-19 by digital innovations: A systematic literature review

The worldwide spread of the COVID-19 disease has had a catastrophic effect on healthcare supply chains. The current manuscript systematically analyzes existing studies mitigating strategies for disruption management in the healthcare supply chain during COVID-19. Using a systematic approach, we recognized 35 related papers. Artificial intelligence (AI), block chain, big data analytics, and simulation are the most important technologies employed in supply chain management in healthcare. The findings reveal that the published research has concentrated mainly on generating resilience plans for the management of COVID-19 impacts. Furthermore, the vulnerability of healthcare supply chains and the necessity of establishing better resilience methods are emphasized in most of the research. However, the practical application of these emerging tools for managing disturbance and warranting resilience in the supply chain has been examined only rarely. This article provides directions for additional research, which can guide researchers to develop and conduct impressive studies related to the healthcare supply chain for different disasters.

Designing a Resilient-Sustainable Supply Chain Network of Age-Differentiated Blood Platelets Using Vertical-Horizontal Transshipment and Grey Wolf Optimizer

Blood platelets are a typical instance of perishable age-differentiated products with a shelf life of five days (on average), which may lead to significant wastage of some collected samples. At the same time, a shortage of platelets may also be observed because of emergency demands and the limited number of donors, especially during disasters such as wars and the COVID-19 pandemic. Therefore, developing an efficient blood platelet supply chain management model is highly necessary to reduce shortage and wastage. In this research, an integrated resilient-sustainable supply chain network of perishable age-differentiated platelets considering vertical and horizontal transshipment is designed. In order to achieve sustainability, economic cost, social cost (shortage), and environmental cost (wastage) are taken into account. A reactive resilient strategy utilizing lateral transshipment between hospitals is adopted to make the blood platelet supply chain powerful against shortage and disruption risks. The presented model is solved using a metaheuristic based on a local search-empowered grey wolf optimizer. The obtained results demonstrate the efficiency of the proposed vertical-horizontal transshipment model in reducing total economic cost, shortage, and wastage by 3.61%, 30.1%, and 18.8%, respectively.

Multi-objective two-stage emergent blood transshipment-allocation in COVID-19 epidemic

The problem of blood transshipment and allocation in the context of the COVID-19 epidemic has many new characteristics, such as two-stage, trans-regional, and multi-modal transportation. Considering these new characteristics, we propose a novel multi-objective optimization model for the two-stage emergent blood transshipment-allocation. The objectives considered are to optimize the quality of transshipped blood, the satisfaction of blood demand, and the overall cost including shortage penalty. An improved integer encoded hybrid multi-objective whale optimization algorithm (MOWOA) with greedy rules is then designed to solve the model. Numerical experiments demonstrate that our two-stage model is superior to one-stage optimization methods on all objectives. The degree of improvement ranges from 0.69 to 66.26%.

Designing an optimal multi-objective model for a sustainable closed-loop supply chain: a case study of pomegranate in Iran

The competitive environment in the global market makes most countries look for better ways to solve their problems. Food waste is the largest concern facing the food security of the world. Not paying attention to process of pomegranate wastes, such as separating the peel from the other parts and ignoring the cost of using artificial intelligence for pest control in gardens and the cost of maintaining the processed products are the gaps of previous researches. To cope with this challenge, recent studies have presented sustainable closed-loop supply chains (SCLSCs) as a strategic approach and a competitive advantage. The present study distinguishes itself from other studies by using the artificial intelligence technology in a supply chain along with the reverse logistics section, i.e., waste recycling. This paper proposes a design for a CLSC pomegranates. The corresponding logistics network is designed for several periods and covers manufacturers, distribution centers, customers, factories, recycling centers (compost centers), and compost end user (compost markets). Using reverse logistics, the wasted pomegranates are also converted into recycled products including ethanol, as an automotive fuel and a renewable energy, and a type of compost processed as an organic fertilizer. The goal of proposed model is to minimize the costs of supply chains, reduce the supply risks involved, and increase the profits for gardeners and investors in the public and non-profit agriculture sectors in Iran. The first pareto solution is 1,869,908.962, 2172.638 and 65.926, and the CPU time is 412 Ms. The results show a rise in the maximum supply risk occurs in the total cost and risk but a reduction in the accountability of the network and also an increase in the disruption period findings in increased total cost and risk of the network, while it first increases and then decreases the accountability.

Design of multimodal hub-and-spoke transportation network for emergency relief under COVID-19 pandemic: A meta-heuristic approach

When COVID-19 suddenly broke out, the epidemic areas are short of basic emergency relief which need to be transported from surrounding areas. To make transportation both time-efficient and cost-effective, we consider a multimodal hub-and-spoke transportation network for emergency relief schedules. Firstly, we establish a mixed integer nonlinear programming (MINLP) model considering multi-type emergency relief and multimodal transportation. The model is a bi-objective one that aims at minimizing both transportation time consumption and transportation costs. Due to its NP-hardness, devising an efficient algorithm to cope with such a problem is challenging. This study thus employs and redesigns Grey Wolf Optimizer (GWO) to tackle it. To benchmark our algorithm, a real-world case is tested with three solution methods which include other two state-of-the-art meta-heuristics. Results indicate that the customized GWO can solve such a problem in a reasonable time with higher accuracy. The research could provide significant practical management insights for related government departments and transportation companies on designing an effective transportation network for emergency relief schedules when faced with the unexpected COVID-19 pandemic.

Designing humanitarian logistics network for managing epidemic outbreaks in disasters using Internet-of-Things. A case study: An earthquake in Salas-e-Babajani city

Along with the destructive effects of catastrophes throughout the world, the COVID-19 outbreak has intensified the severity of disasters. Although the global aid organizations and philanthropists aim to alleviate the adverse impacts, many employed actions are not impactful in dealing with the epidemic outbreak in disasters. However, there is a gap in controlling the epidemic outbreak in the aftermath of disasters. Therefore, this paper proposes a novel humanitarian location-allocation-inventory model by focusing on preventing COVID-19 outbreaks with IoT-based technology in the response phase of disasters. In this study, IoT-based systems enable aid and health-related organizations to monitor people remotely, suspect detection, surveillance, disinfection, and transportation of relief items. The presented model consists of two stages; the first is defining infected cases, transferring patients to temporary hospitals promptly, and accommodating people in evacuation centers. Next, distribution centers are located in the second stage, and relief items are transferred to temporary hospitals and evacuation centers equally regarding shortage minimization. The model is solved by the LP-metric method and applied in a real case study in Salas-e-Babajani city, Kermanshah province. Then, sensitivity analysis on significant model parameters pertaining to the virus, relief items, and capacity has been conducted. Using an IoT-based system in affected areas and evacuation centers reduces the number of infected cases and relief item's shortages. Finally, several managerial insights are obtained from sensitivity analyses provided for healthcare managers.

An agent-based modeling framework for the design of a dynamic closed-loop supply chain network

The supply chain is a dynamic and uncertain system consisting of material, information, and fund flows between different organizations, from the acquisition of the raw materials to the delivery of the finished products to the end customers. Closed-loop supply chains do not end with the delivery of the finished products to the end customers, the process continues until economic value is obtained from the returned products or they are disposed properly in landfills. Incorporating reverse flows in supply chains increases the uncertainty and complexity, as well as complicating the management of supply chains that are already composed of different actors and have a dynamic structure. Since agent-based modeling and simulation is a more efficient method of handling the dynamic and complex nature of supply chains than the traditional analytical methods, in this study agent-based modeling methodology has been used to model a generic closed-loop supply chain network design problem with the aims of integrating customer behavior into the network, coping with the dynamism, and obtaining a more realistic structure by eliminating the required assumptions for solving the model with analytical methods. The actors in the CLSC network have been defined as agents with goals, properties and behaviors. In the proposed model dynamic customer arrivals, the changing aspects of customers' purchasing preferences for new and refurbished products and the time, quantity and quality uncertainties of returns have been handled via the proposed agent-based architecture. To observe the behavior of the supply chain in several conditions various scenarios have been developed according to different parameter settings for the supplier capacities, the rate of customers being affected by advertising, the market incentive threshold values, and the environmental awareness of customers. From the scenarios, it has been concluded that the system should be fed in the right amounts for the new and refurbished products to increase the effectiveness of factors such as advertising, incentives, and environmental awareness for achieving the desired sales amounts and cost targets.

A hybrid model for robust design of sustainable closed-loop supply chain in lead-acid battery industry

Considering supply chain efficiency during the network design process significantly affect chain performance improvement. In this paper, the design process of a sustainable lead-acid battery supply chain network was addressed. Because the design of such networks always involves great computational complexity, in the present study, a two-stage model was proposed to overcome this issue. In the first stage, candidate sites of recycling centers were identified using data envelopment analysis (DEA) and based on their efficiency scores. Unlike the previous studies, not only economic criteria but also technical and geographical criteria were employed to select these locations. In the second stage, a bi-objective programming model was developed to simultaneously determine the tactical and strategic decisions of the chain. Since some data was subject to uncertainty, a robust possibilistic approach was presented. The model ensures that the resulting structure for the chain will be robust to noise and disturbance in parameters. A life cycle assessment model based on the ReCiPe 2008 method was developed in SimaPro software. To evaluate the applicability of the presented method, a case study in the automotive industry was used. The results of implementing the DEA method showed that from among 23 available locations, 11 potential places were selected for construct recycling centers. The final results showed that the inappropriate potential locations of recycling centers were eliminated, and the complexity of the mathematical model proposed in the second stage was reduced. The obtained results of environmental protection costs revealed that this criterion changed from 0 to 8,333,874,332. Moreover, the first objective function resulted in a centralized network to minimize costs, and in contrast, the second objective function tended to decentralize the network to minimize environmental impacts.

Research on the Anti-Risk Mechanism of Mask Green Supply Chain from the Perspective of Cooperation between Retailers, Suppliers, and Financial Institutions

Against the background of the pandemic, the mask supply chain faces the risk of pollution caused by discarded masks, the risk of insufficient funds of retailers, and the risk of mask overstock. To better guard against the above risks, this study constructed a two-party game model and a cusp catastrophe model from the perspective of the mask green supply chain, and studied the strategic choices of retailers and suppliers in the supply chain affected by the risk of capital constraints and overstock. The result shows that the risk shocks will lead to the disruption of the mask green supply chain, and the main factors affecting the strategy choice of mask suppliers and retailers are mask recycling rate, deposit ratio, risk occurrence time, etc. In further research, this study involved a mechanism for financial institutions, mask retailers, and the government to jointly deal with the risk of mask overstock, the risk of retailers' insufficient funds, and the risk of environmental pollution from discarded masks. The research path and conclusion of this study reveal the risks in the circulation area of mask supplies during the pandemic, and provide recommendations for planning for future crises and risk prevention.

A heuristic-based hybrid algorithm to configure a sustainable supply chain network for medical devices considering information-sharing systems

In today's hyper-competitive marketplace, the crucial role of the sustainability concept has been highlighted more. Hence, managers' attention has been attracted to the concept of sustainable supply chains. On the other hand, after the COVID-19 outbreak, the importance of medical devices and their demand has drastically enhanced, which has led to shifting the attention of researchers toward this industry. In this regard, based on the importance of the mentioned points, the current study configures a sustainable supply chain network for the medical devices industry. In this way, given the crucial role of the oxygen concentrator during the COVID-19 outbreak, the present study investigates the supply chain of the mentioned goods as a case study. Also, this research develops an efficient hybrid solution method based on goal programming, a heuristic algorithm, and the simulated annealing algorithm to solve the suggested model. Eventually, sensitivity analysis is conducted to examine the influence of the crucial parameters of the model on the outputs, and managerial insights are provided. According to the achieved results, the suggested model and the developed hybrid method demonstrate a good performance which shows their efficiency.

Designing a sustainable reverse supply chain network for COVID-19 vaccine waste under uncertainty

The vast nationwide COVID-19 vaccination programs are implemented in many countries worldwide. Mass vaccination is causing a rapid increase in infectious and non-infectious vaccine wastes, potentially posing a severe threat if there is no well-organized management plan. This paper develops a mixed-integer mathematical programming model to design a COVID-19 vaccine waste reverse supply chain (CVWRSC) for the first time. The presented problem is based on minimizing the system's total cost and carbon emission. The uncertainty in the tendency rate of vaccination is considered, and a robust optimization approach is used to deal with it, where an interactive fuzzy approach converts the model into a single objective problem. Additionally, a Lagrangian relaxation (LR) algorithm is utilized to deal with the computational difficulty of the large-scale CVWRSC network. The model's practicality is investigated by solving a real-life case study. The results show the gain of the developed integrated network, where the presented framework performs better than the disintegrated vaccine and waste supply chain models. According to the results, vaccination operations and transportation of non-infectious wastes are responsible for a large portion of total cost and emission, respectively. Autoclaving technology plays a vital role in treating infectious wastes. Moreover, the sensitivity analyses demonstrate that the vaccination tendency rate significantly impacts both objective functions. The case study results prove the model's robustness under different realization scenarios, where the average objective function of the robust model is less than the deterministic model ones' in all scenarios. Finally, some insights are given based on the obtained results.

Comparison of hospital service performances under COVID-19 pandemics for pilot regions with low vaccination rates

It is essential to measure the quality and performance of health centers and propose policies in order for health services to continue without interruption during the pandemic period and for the continuous and proper implementation of new procedures in hospitals with COVID-19.The measurement of service quality and performance in hospitals should be provided not only for the smooth flow of health services that are vital for individuals but also for the elimination of hesitations in the treatment and vaccination processes related to COVID-19. Previously, models have been proposed by introducing some criteria to measure and evaluate hospital service performance in some extraordinary conditions, but such a study has not yet been put forward under pandemic conditions. Starting from this point, we aim to fill the gap in the literature by conducting a measurement study for hospitals in the pilot region, where COVID-19 cases are common but vaccination is observed at low rates. For this aim, the evaluation criteria are gathered under basic dimensions as in SERVPERF (Service Performance), which is a widely used tool for measuring service quality and a fuzzy multi-criteria decision analysis is proposed to measure the service performance of state hospitals for a pilot region. In the proposed methodology, the integrated methods consisting of CRITIC-TOPSIS have been extended with fermatean fuzzy sets. Expert opinions are taken via questionaries to determine hospital service performances. Based on the results obtained from the hospitals in the pilot region, the policies and strategies to be adopted by the hospitals serving under pandemic conditions worldwide to increase the service quality have been put forward. Additionally, the sensitivity of the parameters in the problem is measured, and then the validity of the obtained results is also validated. According to the results, assurance is determined as the most important main service performance factor during the pandemic period. So, the managers should develop strategies to address people's concerns about vaccines and increase people's trust in hospitals.

Viable closed-loop supply chain network by considering robustness and risk as a circular economy

The viable closed-loop supply chain network (VCLSCND) is a new concept that integrates sustainability, resiliency, and agility into a circular economy. We suggest a hybrid robust stochastic optimization by minimizing the weighted expected, maximum, and entropic value at risk (EVaR) of the cost function for this problem. This form considers robustness against demand disruption. Finally, CLSC components are located, and quantity flows are determined in the automotive industry. The results show that the VCLSCND cost is less than not considering viability and has a - 0.44% gap. We analyze essential parameters. By increasing the conservative coefficient, confidence level, and the scale of the main model, decreasing the allowed maximum energy, the cost function, time solution, and energy consumption grow. We suggested applying the Fix-and-Optimize algorithm for producing an upper bound for large-scale. As can be seen, the gap between this algorithm and the main problem for cost, energy, and time solution is approximately 6.10%, - 8.28%, and 75.01%.

Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients

The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient's survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient's likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises.

Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations

Grounded in dynamic capabilities, this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs, it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients, the average daily length of stay (LOS), and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19, and includes data from 238,152 patients. Comparing statistics on daily patient volumes, average LOS, and resource usage, both before and during the COVID-19 pandemic, we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period, these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model, we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158,347, it decreased to 79,805 during-COVID-19. On the other hand, while the average daily LOS was 117.53 min before-COVID-19, this value was calculated to be 165,03 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies, it empirically investigates the impact of different policies on ED operations.

Viable healthcare supply chain network design for a pandemic

The recent COVID-19 pandemic revealed that healthcare networks must have a flexible and effective structure. In this study, we develop a viable healthcare network design for a pandemic using a multi-stage stochastic approach. We propose a multi-level network that includes health centers, computed tomography scan centers, hospitals, and clinics. Patients have conditions to returning to normal life or quarantining at home. Three objectives are defined: maximizing the probability of patient recovery, minimizing the costs of all centers in the network, and minimizing the Coronavirus death rate. We investigate a real case study in Iran to demonstrate the model's applicability. Finally, we compare the healthcare supply chain network design in a pandemic with a normal situation to advise how the network can continue to remain viable.

The electric power supply chain network design and emission reduction policy: a comprehensive review

Electrical energy is unique because it must sustain a consistent production and consumption balance. This guarantees that the "generation-transmission-distribution-consumption-storage" electric power supply chain (EPSC) continues to be connected and inseparable through the power system's complete operation. This paper is a type of secondary study. According to the nature of the article which is qualitative, the grounded theory (GT) method for reviewing the literature has been used. A systematic review of existing literature is carried out over 12 years (2010-2022) by classifying it based on several dimensions such as phase sources of production, the total fixed cost of the real variable, and criteria of decision-making and emissions. The literature is further divided into categories based on the year of publication and the journals in which it was published. The study shows a direction for future studies in the electric power domain of the supply chain while utilizing an operations strategy approach.

Data analytics during pandemics: a transportation and location planning perspective

The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.

Adapting supply chain operations in anticipation of and during the COVID-19 pandemic

This article investigates the impacts of the COVID-19 pandemic and their proactive mediation by adaptive operational decisions in different network design structures in anticipation of and during the pandemic. In generalized terms, we contribute to the understanding of the effect of preparedness and recovery decisions in a pandemic setting on supply chain operations and performance. In particular, we examine the impact of inventory pre-positioning in anticipation of a pandemic and the adaptation of production-ordering policy during the pandemic. Our model combines three levels, which is not often seen jointly in operations management literature, i.e., pandemic dynamics, supply chain design, and operational production-inventory control policies. The analysis is performed for both two- and three-stage supply chains and different scenarios for pandemic dynamics (i.e., uncontrolled propagation or controlled dispersal with lockdowns). Our findings suggest that two-stage supply chains exhibit a higher vulnerability in disruption cases. However, they are exposed to a lower system inertia and show positive effects at the recovery stage. Supply chain adaptation ahead of a pandemic is more advantageous than during the pandemic when specific operational recovery policies are deployed. We show that it is instructive to avoid simultaneous changes in structural network design and operational policies since that can destabilize the production-inventory system and result in higher product shortages.

Optimizing firm's supply chain resilience in data-driven business environment

Purpose

This study aims to investigate the impact of a firm’s supply chain capabilities on supply chain resilience, and the impact of supply chain resilience on sustainable supply chain performance in a data-driven business environment. The study also aims to explore the function of supply chain resilience in mediating the relationship between a firm’s supply chain capability and sustainable supply chain performance.

Design/methodology/approach

Primary data were acquired through a survey of 310 managers of small- and medium-sized businesses in a variety of industries across Bangladesh. The data were analyzed using partial least squares structural equation modeling.

Findings

A firm’s supply chain capabilities include information technology, leadership and collaboration. Supply chain capability is positively associated with supply chain resilience. The resilience of a firm’s supply chain is also positively correlated with its sustainable supply chain performance. Supply chain resilience plays a mediating role in the relationship between a firm’s supply chain capabilities and its sustainable supply chain performance.

Research limitations/implications

This study provides a theoretical contribution by corroborating practical knowledge focusing on firms’ supply chain capability, supply chain resilience and sustainable supply chain performance by using a resource-based view and dynamic capability theory – a relevant and unexplored subject in the supply chain literature – and proposes several opportunities for future research.

Practical implications

The results highlight the study’s managerial and social relevance from the perspective of firms in developing countries. As firms shift toward an online environment, managers and decision-makers need to make strategic decisions, as they did to overcome the challenges presented by COVID-19.

Originality/value

The study’s findings demonstrate that firms’ supply chain capabilities can be leveraged to increase supply chain resilience. Firms’ resilience during COVID-19 allowed them to avoid losses and to improve their supply chain’s sustainable performance. To the best of the authors’ knowledge, their complex higher order model is a unique contribution to the literature on firms’ supply chain capability and extends previous research on this topic.

A new humanitarian relief logistic network for multi-objective optimization under stochastic programming

Millions of affected people and thousands of victims are consequences of earthquakes, every year. Therefore, it is necessary to prepare a proper preparedness and response planning. The objectives of this paper are i) minimizing the expected value of the total costs of relief supply chain, ii) minimizing the maximum number of unsatisfied demands for relief staff and iii) minimizing the total probability of unsuccessful evacuation in routes. In this paper, a scenario based stochastic multi-objective location-allocation-routing model is proposed for a real humanitarian relief logistics problem which focused on both pre- and post-disaster situations in presence of uncertainty. To cope with demand uncertainty, a simulation approach is used. The proposed model integrates these two phases simultaneously. Then, both strategic and operational decisions (pre-disaster and post-disaster), fairness in the evacuation, and relief item distribution including commodities and relief workers, victim evacuation including injured people, corpses and homeless people are also considered simultaneously in this paper. The presented model is solved utilizing the Epsilon-constraint method for small- and medium-scale problems and using three metaheuristic algorithms for the large-scale problem (case study). Empirical results illustrate that the model can be used to locate the shelters and relief distribution centers, determine appropriate routes and allocate resources in uncertain and real-life disaster situations.

Analysis of critical barriers in the sustainable supply chain of MSMEs: a case of Makhana (Foxnut) industry

Purpose

Sustainability is a very important factor to be considered in the supply chain (SC) of any industry. Agricultural industry needs to be addressed even more importantly with the tools of sustainability as it concerns the life of millions. This paper explores the critical barriers (CBs) in the sustainable supply chains (SSCs) of makhana industry located in the northern part of India and seeks to design a model for the researchers and the managers who want to work in this industry.

Design/methodology/approach

Initially, the CBs were identified with the help of an extensive literature review of sustainability in SCs for agri-industry and discussion with makhana industry experts (consisting of managers and senior managers) and academicians (consisting of professors and research scholars). The study uses the multi-criteria decision-making (MCDM) technique, namely interpretive structural modeling (ISM) and fuzzy ISM to develop the model. The study finally validates the model using Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis.

Findings

The obtained results indicate that, in the SSC of makhana industry, the role of “Lack of adoption of organic agricultural management techniques” (CB2), “Lack of modern techniques (CB4)”, “Multiple intermediaries” (CB5), “Weak socio-economic conditions” (CB7) and “Lack of proper knowledge” (CB1) are very significant. These barriers are needed to be addressed first as they have the highest driving power and other barriers are directly driven by these CBs.

Research limitations/implications

The paper has included seven experts, and the interrelationship between CBs has been developed on the basis of their knowledge and discussion, so the results may be a little bias. Moreover, the paper has obtained the results using the ISM and fuzzy ISM by considering ten CBs; the researchers can explore this research by including more CBs and validate the results using other MCDM techniques like fuzzy-decision making trial and evaluation laboratory (DEMATEL), fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Best Worst Method (BWM).

Originality/value

This study is unique as per industry point of view and may help the researchers and managers to explore the field of makhana.

An enhanced honey badger algorithm based on Lévy flight and refraction opposition-based learning for engineering design problems

Honey badger algorithm (HBA) is a recently developed meta-heuristic algorithm, which mainly simulates the dynamic search behavior of honey badger in wild nature. Similar to other basic algorithms, HBA may suffer from the weakness of poor convergence accuracy, inadequate balance between exploration and exploitation, and ease of getting trapped into the local optima. In order to address these drawbacks, this paper proposes an enhanced honey badger algorithm (EHBA) to improve the search quality of the basic method from three aspects. First, we introduce the highly disruptive polynomial mutation to initialize the population. This is considered from increasing the population diversity. Second, Lévy flight is integrated into the position update formula to boost search efficiency and balance exploration and exploitation capabilities of the algorithm. Furthermore, the refraction opposition-based learning is applied to the current global optimum of the swarm to help the population jump out of the local optima. To validate the function optimization performance, the proposed EHBA is comprehensively analyzed on 18 standard benchmark functions and IEEE CEC2017 test suite. Compared with the basic HBA and seven state-of-the-art algorithms, the experimental results demonstrate that EHBA can outperform other competitors on most of the test functions with superior solution accuracy, local optima avoidance, and stability. Additionally, the applicability of the proposed method is further highlighted by solving four engineering design problems. The results indicate that EHBA also has competitive performance and promising prospects for real-world optimization tasks.

Prioritization of healthcare systems during pandemics using Cronbach's measure based fuzzy WASPAS approach

Pandemics are well-known as epidemics that spread globally and cause many illnesses and mortality. Because of globalization, the accelerated occurrence and circulation of new microbes, the infection has emerged and the incidence and movement of new microbes have sped up. Using technological devices to minimize the visit durations, specifying days for handling chronic diseases, subsidy for the staff are the alternatives that can help prevent healthcare systems from collapsing during pandemics. The study aims to define the efficient usage of optimization tools during pandemics to prevent healthcare systems from collapsing. In this study, a new integrated framework with fuzzy information is developed, which attempts to prioritize these alternatives for policymakers. First, rating data are assigned respective fuzzy values using the standard singleton grades. Later, criteria weights are determined by extending Cronbach´s measure to fuzzy context. The measure not only understands data consistency comprehensively, but also takes into consideration the attitudinal characteristics of experts. By this approach, a rational weight vector is obtained for decision-making. Further, an improved Weighted Aggregated Sum Product Assessment (WASPAS) algorithm is put forward for ranking alternatives, which is flexibly considering criteria along with personalized ordering and holistic ordering alternatives. The usefulness of the developed framework is tested with the help of a real case study. Rank values of alternatives when unbiased weights are used is given by 0.741, 0.582, 0.640 with ordering asR 1 ≻ R 3 ≻ R 2 . The sensitivity/comparative analysis reveals the impact of the proposed model as useful in selecting the best alternative for the healthcare systems during pandemics.

Colorectal cancer: Artificial intelligence and its role in surgical decision making

Despite several advances in the oncological management of colorectal cancer (CRC), there still remains a lacuna in the treatment strategy, which differs from center to center and on the philosophy of the treating clinician that is not without bias. Personalized treatment is essential for the treatment of CRC to achieve better long-term outcomes and to reduce morbidity. Surgery has an important role to play in the treatment. Surgical treatment of CRC is decided based on clinical parameters and investigations and hence likely to have judgmental errors. Artificial intelligence has been reported to be useful in the surveillance, diagnosis, treatment, and follow-up with accuracy in several malignancies. However, it is still evolving and yet to be established in surgical decision making in CRC. It is not only useful preoperatively but also intraoperatively. Artificial intelligence helps to rectify the human surgical decision when clinical data and radiological and laboratory parameters are fed into the computer and may guide correct surgical treatment.

The organizational side of a disruption mitigation process: exploring a case study during the COVID-19 pandemic

This paper deals with the mitigation process of the COVID-19 pandemic. Scholars propose and discuss several mitigation strategies to face the COVID-19 disruptions, mainly focusing on technology and supply chain redesign related aspects. Less attention has been paid to the organizational aspects of the mitigation process. We address this gap through an in-depth analysis of the reactive organizational practices implemented by an Italian company during the COVID-19 pandemic. We further compare these practices with those proposed in the disruption management literature to identify common traits and differences. The results show that the overall management of a pandemic’s mitigation process does not significantly differ from that of conventional disruptions, since both contexts require the same basic organizational practices. However, some peculiarities on how these practices should be implemented in a pandemic setting do emerge, such as the implementation of a cyclic rather than linear problem-solving process, the adoption of a learning-by-doing approach, the need of a risk-taker mindset and the importance of creativity and improvisation. Besides complementing the literature, these findings allow to provide indications to managers on how to organize and coordinate the activities during the mitigation process, as well as on what capabilities and competencies should be leveraged to face the pandemic’s disruptions.

A Dynamic Analysis for Mitigating Disaster Effects in Closed Loop Supply Chains

The increased level of complexity in the case of Closed Loop Supply Chains (CLSCs) turns them into vulnerable systems under a disaster event. The latter calls for a methodological approach that allows a dynamic study under alternative policies in mitigating the disaster effects with a focus on creating sustainable CLSCs. For this reason, we provide a System Dynamics (SD)-based analysis for disaster events on the operation of CLSCs. By “disaster event”, we mean three different categories taking shape on the basis of duration. Furthermore, three different demand patterns emerging due to the disaster event are examined. We assume that the disaster event affects the manufacturer, and we examine the system response under different mitigation policies. For each demand pattern two different mitigation policies at the manufacturer level are examined by considering the total CLSC profit and demand backlog as measures of policy performance. For each combination, extensive simulation experimentation reveals sustainable policy recommendations under alternative settings regarding the reduction in the manufacturer’s production.

Enhancing the performance of Indian micro, small and medium enterprises by implementing supply chain finance: challenges emerging from COVID-19 pandemic

Purpose

The already scarce financial resources coupled with the current COVID-19 pandemic have created the worst scenario for Indian micro, small and medium enterprises (MSMEs). The application of supply chain finance (SCF) solutions to MSMEs can enhance the performance and growth of the sector. But, the implementation of SCF solutions faces various obstacles which restrict the MSMEs' ability to meet their financial requirements. The purpose of this paper is to explore and prioritize the various important barriers hindering SCF application in Indian MSMEs.

Design/methodology/approach

Literature on SCF and MSMEs are critically reviewed and barriers affecting the SCF application in Indian MSMEs are scrutinized with the consultation of the experts. The present study applies intuitionistic fuzzy-analytic hierarchy process (IF-AHP) methodology to prioritize the identified barriers and thereafter, the sensitivity analysis is also done to observe the identified barriers under different situations.

Findings

The results of the study have revealed that poor cash flow management and working capital management disruption are acting as the most prioritized barriers of SCF. The external factor of cultural challenges has been prioritized as the minimum-influence factor that has least negative influence on the operations of SCF in MSMEs.

Practical implications

The present study bears an important practical and managerial implication to solve real world problems of financial constraints of MSMEs. The managers should emphasize upon the importance smooth flow of cash and working capital management across the supply chains by which better SCF solution can be implemented in MSMEs.

Originality/value

The study conducted is an effort to address the barriers of SCF in Indian MSMEs during the COVID-19 pandemic. The implementation of IF-AHP and sensitivity analysis would help managers and policymakers to comprehend and resolve the prioritized barriers and sub-barriers of SCF in the MSMEs.

Organizational e-Health Readiness: How to Prepare the Primary Healthcare Providers’ Services for Digital Transformation

The COVID-19 pandemic has had two main consequences for the organization of treatment in primary healthcare: restricted patients’ access to medical facilities and limited social mobility. In turn, these consequences pose a great challenge for patients and healthcare providers, i.e., the limited personal contact with medical professionals. This can be eased by new digital technology. While providing solutions to many problems, this technology poses several organizational challenges for healthcare system participants. As the current global situation and the outbreak of the humanitarian crisis in Europe show, these and other likely emergencies amplify the need to learn the lessons and prepare organizations for exceptional rapid changes. Therefore, a question arises of whether organizations are ready to use modern e-health solutions in the context of a rapidly and radically changing situation, and how this readiness can be verified. The aim of this article is to clarify the organizational e-heath readiness concept of Polish primary healthcare units. This study employs the triangulation of analytical methods, as it uses: (i) a literature review of e-health readiness assessment, (ii) primary data obtained with a survey (random sampling of 371 managers of PHC facilities across Poland) and (iii) the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, employed to estimate the structural model. The evaluation of the model revealed that its concept was adequate for more mature entities that focus on the patient- and employee-oriented purpose of digitization, and on assuring excellent experience derived from a consistent care process. In the context of patients’ restricted access to medical facilities and limited social mobility, a simpler version of the research model assesses the readiness more adequately. Finally, the study increases the knowledge base of assets (resources and capabilities), which will help healthcare systems better understand the challenges surrounding the adoption and scaling of e-health technologies.

Purchase intention toward sustainable masks after COVID-19: the moderating role of health concern

This study aimed to investigate consumers’ intentions to purchase sustainable masks to reduce the environmental pollution caused by disposable masks in the context of COVID-19. A research model was derived based on the Value-Belief-Norm theory and the Theory of Planned Behavior, and the moderating role of health concerns and environmental knowledge due to the COVID-19 pandemic were examined. Through a Korean online survey company, we collected data on sustainable masks from respondents aged from their 20 s to 50 s, living in the Korea, and a structural equation analysis was performed on the 337 valid samples. Environmental concerns and beliefs were found to have a positive impact on the purchase intention on sustainable masks. Although environmental knowledge played the role of a moderator, we found that the higher the health concern, the stronger the purchase intention. Based on these results, it is possible to derive a strategy to increase the purchase of sustainable masks and reduce the environmental pollution caused by disposable masks. A sales strategy should be implemented for groups with high health concern. In addition, since the subjective norm increases the purchase intention for sustainable masks, advertising that stimulates them will help reduce environmental pollution caused by disposal masks. In the future, it will be possible to help reduce environmental pollution not only during the COVID-19 pandemic, but also during other emerging pandemics.

Public concerns and burdens associated with face mask-wearing: Lessons learned from the COVID-19 pandemic

This study attempts to identify and categorize the key concerns of wearing masks. An online survey was used to collect data from 2746 people in the United States. Results show that the mask-wearing concerns can be classified into three categories; discomfort barriers (physical discomfort and communication discomfort), external factors (overstated news about coronavirus threat, political beliefs, and absence of mask-wearing culture), and usability issues (lack of effectiveness, unnecessariness of masks in certain cases, and mask maintenance issues). The findings demonstrate that all mentioned concerns strongly shape people's attitudes toward wearing masks, except for political beliefs and lack of effectiveness.