Reverse logistics network design for medical waste management in the epidemic outbreak of the novel coronavirus (COVID-19)

Optimization of household medical waste recycling logistics routes: Considering contamination risks

The escalating generation of household medical waste, a byproduct of industrialization and global population growth, has rendered its transportation and logistics management a critical societal concern. This study delves into the optimization of routes for vehicles within the household medical waste logistics network, a response to the imperative of managing this waste effectively. The potential for environmental and public health hazards due to improper waste disposal is acknowledged, prompting the incorporation of contamination risk, influenced by transport duration, waste volume, and wind velocity, into the analysis. To enhance the realism of the simulation, traffic congestion is integrated into the vehicle speed function, reflecting the urban roads' variability. Subsequently, a Bi-objective mixed-integer programming model is formulated to concurrently minimize total operational costs and environmental pollution risks. The complexity inherent in the optimization problem has motivated the development of the Adaptive Hybrid Artificial Fish Swarming Algorithm with Non-Dominated Sorting (AH-NSAFSA). This algorithm employs a sophisticated approach, amalgamating congestion distance and individual ranking to discern optimal solutions from the population. It incorporates a decay function to facilitate an adaptive iterative process, enhancing the algorithm's convergence properties. Furthermore, it leverages the concept of crossover-induced elimination to preserve the genetic diversity and overall robustness of the solution set. The empirical evaluation of AH-NSAFSA is conducted using a test set derived from the Solomon dataset, demonstrating the algorithm's capability to generate feasible non-dominated solutions for household medical waste recycling path planning. Comparative analysis with the Non-dominated Sorted Artificial Fish Swarm Algorithm (NSAFSA) and Non-dominated Sorted Genetic Algorithm II (NSGA-II) across metrics such as MID, SM, NOS, and CT reveals that AH-NSAFSA excels in MID, SM, and NOS, and surpasses NSAFSA in CT, albeit slightly underperforming relative to NSGA-II. The study's holistic approach to waste recycling route planning, which integrates cost-effectiveness with pollution risk and traffic congestion considerations, offers substantial support for enterprises in formulating sustainable green development strategies. AH-NSAFSA offers an eco-efficient, holistic approach to medical waste recycling, advancing sustainable management practices.

Economic evaluation of hazardous healthcare waste treatment systems

The cost estimation and assessment of healthcare waste treatment systems (HCWTSs) for preventing financial and environmental damage are essential. This work reports economic analyses of treatment of hazardous-infectious waste based on WHO approach in HCWTSS of 43 hospitals in Tehran, Iran. The waste generation rate for total hospital waste in 43 HCWTSS was 4.42 ± 2.77 kg/active-bed/day. The mean of chemical, sharps, infectious, and general wastes in 43 HCWTSS were 13.79 ± 19.71, 30.29 ± 37.46, 336.28 ± 291.31, and 539.6 ± 383.13 kg/day, respectively. Economic analyses proved that general hospitals spent 1.63 times more than specialized hospitals on treating hazardous-infectious waste per year. The annual cost of treating each kilogram of hazardous healthcare waste in studied HCWTSS was 0.3 dollars. A range of total annual costs in 43 HCWTSS was limited to 7.9-118 thousand dollars. The results of ANOVA test demonstrated that the age and performance levels of hospitals significantly affect the annual capital and operating costs, respectively. Hence, improving recycling knowledge and increasing source-separated recycling should be considered to control the costs in HCWTSS. The results of this work have implications for the hospital managers in especially developing countries to evaluate previously unknown economic analyses and policies and take action to control wasted costs in HCWTSS.

Global Health Commodities Supply Chain in the Era of COVID-19 Pandemic: Challenges, Impacts, and Prospects: A Systematic Review

Background

The COVID-19 pandemic led to the most substantial health crisis in the 21st Century. This pandemic interrupted the supply of essential commodities for human beings. Among the essential commodities for human survival, disruption of the supply of essential health commodities has become a global concern.

Objective

The study aimed to systematically analyze published articles on the challenges, impacts, and prospects of the global health commodities' supply chain in the era of the COVID-19 pandemic.

Methods

A standard searching strategy was conducted in seven research databases to retrieve pertinent articles. Finally, 459 articles were retrieved for further screening, and only 13 articles were selected for final synthesis.

Results

Almost 38.5% of the studies targeted the supply chain of health commodities used to treat HIV, TB, and malaria. Lockdown policies, travel restrictions, lack of transportation, low manufacturing capacity, and rising costs were the significant challenges indicated for the supply interruption of essential health commodities and COVID-19 vaccines. Findings indicated that the supply interruption of essential health commodities leads to a devastating impact on global health.

Conclusion

Global medicine shortages due to the pandemic crisis can have a devastatingly harmful impact on patient outcomes and might result in a devastatingly long-lasting effect on the health of the world community. Supply-related challenges of the COVID-19 vaccine affect countries' ambitions for achieving herd immunity quickly. Monitoring the pandemic's effect on the health commodities' supply system and designing a short-term and long-term resilient health supply chain system that can cope with current and future health catastrophes is pivotal.

An innovative medical waste management system in a smart city using XAI and vehicle routing optimization

Background

The management of medical waste is a complex task that necessitates effective strategies to mitigate health risks, comply with regulations, and minimize environmental impact. In this study, a novel approach based on collaboration and technological advancements is proposed.

Methods

By utilizing colored bags with identification tags, smart containers with sensors, object recognition sensors, air and soil control sensors, vehicles with Global Positioning System (GPS) and temperature humidity sensors, and outsourced waste treatment, the system optimizes waste sorting, storage, and treatment operations. Additionally, the incorporation of explainable artificial intelligence (XAI) technology, leveraging scikit-learn, xgboost, catboost, lightgbm, and skorch, provides real-time insights and data analytics, facilitating informed decision-making and process optimization.

Results

The integration of these cutting-edge technologies forms the foundation of an efficient and intelligent medical waste management system. Furthermore, the article highlights the use of genetic algorithms (GA) to solve vehicle routing models, optimizing waste collection routes and minimizing transportation time to treatment centers.

Conclusions

Overall, the combination of advanced technologies, optimization algorithms, and XAI contributes to improved waste management practices, ultimately benefiting both public health and the environment.

Designing a new sustainable Test Kit supply chain network utilizing Internet of Things

The advent of COVID-19 put much economic pressure on countries worldwide, especially low-income countries. Providing test kits for Covid-19 posed a huge challenge at the beginning of the pandemic. Especially the low-income and less developed countries that did not have the technology to produce this kit and had to import it into the country, which itself cost a lot to buy and distribute these kits. This paper proposes a sustainable COVID-19 test kits supply chain network (STKSCN) for the first time to fill this gap. Distribution and transportation of test kits, location of distribution centers, and management of used test kits are considered in this network. A mixed integer linear programming Multi-Objective (MO), multi-period, multi-resource mathematical model is extended for the proposed supply chain. Another contribution is designing a platform based on the Internet of Things (IoT) to increase the speed, accuracy and security of the network. In this way, patients set their appointment online by registering their personal details and clinical symptoms. An augmented ɛ-constraint2 (AUGMECON2) is proposed for solving small and medium size of problem. Also, two meta-heuristic algorithms, namely NSGA-II and PESA-II are presented to solve the small, medium and large size of the problem. Taguchi method is utilized to control the parameters, and for comparison between meta-heuristic, five performance metrics are suggested. In addition, a case study in Iran is presented to validate the proposed model. Finally, the results show that PESA-II is more efficient and has better performance than the others based on assessment metrics and computational time.

Designing reverse logistics network for healthcare waste management considering epidemic disruptions under uncertainty

Population growth and recent disruptions caused by COVID-19 and many other man-made or natural disasters all around the world have considerably increased the demand for medical services, which has led to a rise in medical waste generation. The improper management of these wastes can result in a serious threat to living organisms and the environment. Designing a reverse logistics network using mathematical programming tools is an efficient and effective way to manage healthcare waste. In this regard, this paper formulates a bi-objective mixed-integer linear programming model for designing a reverse logistics network to manage healthcare waste under uncertainty and epidemic disruptions. The concept of epidemic disruptions is employed to determine the amount of waste generated in network facilities; and a Monte Carlo-based simulation approach is used for this end. The proposed model minimizes total costs and population risk, simultaneously. A fuzzy goal programming method is developed to deal with the uncertainty of the model. A simulation algorithm is developed using probabilistic distribution functions for generating data with different sizes; and then used for the evaluation of the proposed model. Finally, the efficiency of the proposed model and solution approach is confirmed using the sensitivity analysis process on the objective functions' coefficients.

A data-driven optimization model to response to COVID-19 pandemic: a case study

COVID-19 is a highly prevalent disease that has led to numerous predicaments for healthcare systems worldwide. Owing to the significant influx of patients and limited resources of health services, there have been several limitations associated with patients' hospitalization. These limitations can cause an increment in the COVID-19-related mortality due to the lack of appropriate medical services. They can also elevate the risk of infection in the rest of the population. The present study aims to investigate a two-phase approach to designing a supply chain network for hospitalizing patients in the existing and temporary hospitals, efficiently distributing medications and medical items needed by patients, and managing the waste created in hospitals. Since the number of future patients is uncertain, in the first phase, trained Artificial Neural Networks with historical data forecast the number of patients in future periods and generate scenarios. Through the use of the K-Means method, these scenarios are reduced. In the second phase, a multi-objective, multi-period, data-driven two-stage stochastic programming is developed using the acquired scenarios in the previous phase concerning the uncertainty and disruption in facilities. The objectives of the proposed model include maximizing the minimum allocation-to-demand ratio, minimizing the total risk of disease spread, and minimizing the total transportation time. Furthermore, a real case study is investigated in Tehran, the capital of Iran. The results showed that the areas with the highest population density and no facilities near them have been selected for the location of temporary facilities. Among temporary facilities, temporary hospitals can allocate up to 2.6% of the total demand, which puts pressure on the existing hospitals to be removed. Furthermore, the results indicated that the allocation-to-demand ratio can remain at an ideal level when disruptions occur by considering temporary facilities. Our analyses focus on: (1) Examining demand forecasting error and generated scenarios in the first phase, (2) exploring the impact of demand parameters on the allocation-to-demand ratio, total time and total risk, (3) investigating the strategy of utilizing temporary hospitals to address sudden changes in demand, (4) evaluating the effect of disruption to facilities on the supply chain network.

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.

TODIM-VIKOR method based on hybrid weighted distance under probabilistic uncertain linguistic information and its application in medical logistics center site selection

At a time of global epidemic control, the location of the medical logistics distribution center (MLDC) has an important impact on the operation of the entire logistics system to reduce the operating costs of the company, enhance the service quality and effectively control the COVID-19 on the premise of increasing the company's profits. Thus, the research on the location of MLDC has important theoretical and practical application significance separately. Recently, the TODIM and VIKOR method has been used to solve multiple-attribute group decision-making (MAGDM) issues. The probabilistic uncertain linguistic term sets (PULTSs) are used as a tool for characterizing uncertain information. In this paper, we design the TODIM-VIKOR model to solve the MAGDM in PULT condition. Firstly, some basic concept of PULTSs is reviewed, and TODIM and VIKOR method are introduced. The extended TODIM-VIKOR model is proposed to tackle MAGDM problems under the PULTSs. At last, a numerical case study for medical logistics center site selection (MLCSS) is given to validate the proposed method.

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.

An atmospheric microwave plasma-based distributed system for medical waste treatment

Inadequate handling of infectious medical waste may promote the spread of the virus through secondary transmission during the transfer process. Microwave plasma, an ease-of-use, device-compact, and pollution-free technology, enables the on-site disposal of medical waste, thereby preventing secondary transmission. We developed atmospheric-pressure air-based microwave plasma torches with lengths exceeding 30 cm to rapidly treat various medical wastes in situ with nonhazardous exhaust gas. The gas compositions and temperatures throughout the medical waste treatment process were monitored by gas analyzers and thermocouples in real time. The main organic elements in medical waste and their residues were analyzed by an organic elemental analyzer. The results showed that (i) the weight reduction ratio of medical waste achieved a maximum value of 94%; (ii) a water-waste ratio of 30% was beneficial for enhancing the microwave plasma treatment effect for medical wastes; and (iii) substantial treatment effectiveness was achievable under a high feeding temperature (≥ 600 °C) and a high gas flow rate (≥ 40 L/min). Based on these results, we built a miniaturized and distributed pilot prototype for microwave plasma torch-based on-site medical waste treatment. This innovation could fill the gap in the field of small-scale medical waste treatment facilities and alleviate the existing issue of handling medical waste on-site.

Investigation of gauze and medical bottle co-pyrolysis on the product formation, reactivity, and reaction pathway of char, liquid oil, and gas

Effective in-site treatment of medical waste has become a weak link in hospitals. Pyrolysis technology is a treatment method for medical waste that can enable rapid disposal in hospital settings and relieve environmental pressure, while also producing high-value products and reducing disposal costs. In this work, the effects of feedstock ratio and temperature on product yield and components of gauze (GA) and medical bottles (MB) co-pyrolysis have been investigated. The higher yield of solid products was obtained by co-pyrolysis of GA and MB at 400 ℃. With the addition of MB and an increase in temperature for the co-pyrolysis of GA and MB in a similar ratio, the pyrolysis oil and gas yields gradually increased. According to GC-MS analysis, co-feeding 75% MB to GA improved the alcohol content from 33.21% to a maximum yield of 59.8% at a pyrolysis temperature of 700 ℃. The content of aliphatic hydrocarbon reached 38.68% when the pyrolysis temperature and MB addition ratio were 700 °C and 75%, respectively. The GC data shows that the main gas components of co-pyrolysis of GA/MB were CH4 and H2, while the pyrolysis of pure GA or MB resulted in CO or CO2. Additionally, the solid carbon products obtained have an excellent pore structure. This strategy can benefit medical waste control and resource utilization for the low-cost disposal of medical waste and the acquisition of high-value resource products.

Graphical Abstract

Logistics performance: critical factors in the implementation of end-of-life management practices in the pharmaceutical care process

The management of healthcare waste and end-of-life medication coming from different sources are primary challenges faced by public health leaders. Several factors may be considered critical and inhibitive to reverse logistics within the context of waste management processes. If those factors are not addressed, they may become obstacles to reverse logistics implementation. The purpose of this study is to evaluate the effect that critical factors play in the adoption of end-of-life management practices for medication and its influence on logistics performance. Literature provided some critical factors: management factor, collaboration factor, information technology factor, infrastructure factor, politics factor, financial and economic factor, end-of-life management practices, and logistics performance factor. A sample of 67 professionals from the public pharmaceutical care process answered a structured questionnaire. The collected data was analyzed using partial least square-structural equation modeling. The theoretical structural test confirmed eleven out of the fifteen hypotheses considered. The results have indicated that end-of-life management practices exert a direct influence on logistics performance. The analysis confirmed a direct effect of the information technology factor on end-of-life management practices, but not a moderation effect. Findings have contributed to the literature by providing deeper insights into the relationship between end-of-life management practices for medicines and logistics performance. Moreover, it supports health managers' decision-making in the pharmaceutical care process improvement and engagement with solid waste management policies.

Infectious waste management during a pandemic: A stochastic location-routing problem with chance-constrained time windows

The COVID-19 pandemic has presented tremendous challenges to the world, one of which is the management of infectious waste generated by healthcare activities. Finding cost-efficient services with minimum threats to public health has become a top priority. The pandemic has induced extreme uncertainties, not only in the amount of generated waste, but also in the associated service times. With this in mind, the present study develops a mixed-integer linear programming (MILP) model for the location-routing problem with time windows (LRPTW). To handle the uncertainty in the amount of generated waste, three scenarios are defined respectively reflecting different severity levels of a pandemic. Furthermore, chance constraints are applied to deal with the variation of the service times at small generation nodes, and time windows at the transfer facilities. The complexity of the resulting mathematical model motivated the application of a branch-and-price (B&P) algorithm along with an ɛ -constraint technique. A case study of the situation of Wuhan, China, during the initial COVID-19 outbreak is employed to examine the performance and applicability of the proposed model. Our numerical tests indicate that the B&P algorithm outperforms CPLEX in the computational times by more than 83% in small-sized problem instances and reduces the gaps by at least 70% in large-scale ones. Through a comparison with the current and deterministic systems, our proposed stochastic system can timely adjust itself to fulfill nearly four times the demand of other systems in an extreme pandemic scenario, while maintaining a cost-efficient operation with no outbreak.

Two-phase COVID-19 medical waste transport optimisation considering sustainability and infection probability

A safe and effective medical waste transport network is beneficial to control the COVID-19 pandemic and at least decelerate the spread of novel coronavirus. Seldom studies concentrated on a two-phase COVID-19 medical waste transport in the presence of multi-type vehicle selection, sustainability, and infection probability, which is the focus of this paper. This paper aims to identify the priority of sustainable objectives and observe the impacts of multi-phase and infection probability on the results. Thus, such a problem is formulated as a mixed-integer programming model to minimise total potential infection risks, minimise total environmental risks, and maximise total economic benefits. Then, a hybrid solution strategy is designed, incorporating a lexicographic optimisation approach and a linear weighted sum method. A real-world case study from Chongqing is used to illustrate this methodology. Results indicate that the solution strategy guides a good COVID-19 medical waste transport scheme within 1 min. The priority of sustainable objectives is society, economy, and environment in the first and second phases because the total Gap of case No.35 is 3.20%. A decentralised decision mode is preferred to design a COVID-19 medical waste transport network at the province level. Whatever the infection probability is, infection risk is the most critical concern in the COVID-19 medical waste clean-up activities. Environmental and economic sustainability performance also should be considered when infection probability is more than a certain threshold.

Large-scale emergency medical services scheduling during the outbreak of epidemics

This paper studies a new large-scale emergency medical services scheduling (EMSS) problem during the outbreak of epidemics like COVID-19, which aims to determine an optimal scheduling scheme of emergency medical services to minimize the completion time of nucleic acid testing to achieve rapid epidemic interruption. We first analyze the impact of the epidemic spread and assign different priorities to different emergency medical services demand points according to the degree of urgency. Then, we formulate the EMSS as a mixed-integer linear program (MILP) model and analyze its complexity. Given the NP-hardness of the problem, we develop two fast and effective improved discrete artificial bee colony algorithms (IDABC) based on problem properties. Experimental results for a real case and practical-sized instances with up to 100 demand points demonstrate that the IDABC significantly outperforms MILP solver CPLEX and two state-of-the-art metaheuristic algorithms in both solution quality and computational efficiency. In addition, we also propose some managerial implications to support emergency management decision-making.

How can infectious medical waste be forecasted and transported during the COVID-19 pandemic? A hybrid two-stage method

The COVID-19 pandemic has caused an unforeseen collapse of infectious medical waste (IMW) and an abrupt smite of the conveying chain. Hospitals and related treatment centers face great challenges during the pandemic because mismanagement may lead to more severe life threats and enlarge environmental pollution. Opportune forecasting and transportation route optimization, therefore, are crucial to coping with social stress meritoriously. All related hospitals and medical waste treatment centers (MWTCs) should make decisions in perspective to reduce the economic pressure and infection risk immensely. This study proposes a hybrid dynamic method, as follows: first to forecast confirmed cases via infectious disease modeling and analyze the association between IMW outflows and cases; next to construct a model through time-varying factors and the lagging factor to predict the waste quantity; and then to optimize the transportation network route from hospitals to MWTCs. For demonstration intentions, the established methodology is employed to an illustrative example. Based on the obtained results, in finding the process of decision making, cost becomes the common concern of decision-makers. Actually, the infection risk among publics has to be considered simultaneously. Therefore, realizing early warning and safe waste management has an immensely positive effect on epidemic stabilization and lifetime health.

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.

Blood plasma supply chain planning to respond COVID-19 pandemic: a case study

The COVID-19 pandemic causes a severe threat to human lives worldwide. Convalescent plasma as supportive care for COVID-19 is critical in reducing the death rate and staying in hospitals. Designing an efficient supply chain network capable of managing convalescent plasma in this situation seems necessary. Although many researchers investigated supply chains of blood products, no research was conducted on the planning of convalescent plasma in the supply chain framework with specific features of COVID-19. This gap is covered in the current work by simultaneous regular and convalescent plasma flow in a supply chain network. Besides, due to the growing importance of environmental problems, the resulting carbon emission from transportation activities is viewed to provide a green network. In other words, this study aims to plan the integrated green supply chain network of regular and convalescent plasma in the pandemic outbreak of COVID-19 for the first time. The presented mixed-integer multi-objective optimization model determines optimal network decisions while minimizing the total cost and total carbon emission. The Epsilon constraint method is used to handle the considered objectives. The model is applied to a real case study from the capital of Iran. Sensitivity analyses are carried out, and managerial insights are drawn. Based on the obtained results, product demand impacts the objective functions significantly. Moreover, the systems' total carbon emission is highly dependent on the flow of regular plasma. The results also reveal that changing transportation emission unit causes significant variation in the total emission while the total cost remains fixed.

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.

COVID-19 and environmental health: A systematic analysis for the global burden of biomedical waste by this epidemic

Since the beginning of this outbreak, much evidence stated that the climb in the amount of biomedical waste harmed human health and had adverse effects on the environment. With the increase of cases of COVID-19 all around the globe, the amount of biomedical waste was also constantly rising. Also, many solutions regarding either reducing or recycling biomedical waste. However, the potential global burden of biomedical waste during this pandemic was not yet been analyzed. Herein, we perform a systematic review of literature on these modalities, including mentioning types of biomedical waste, the effect on health, the environment, and methods of handling biomedical waste during this pandemic. A total of 3551 published papers were identified by two databases. In the end, 15 references were selected for this systematic analysis. Most of the included studies focus on research on the impact of medical waste caused by the COVID-19 pandemic on the environment. The total biomedical waste during the COVID-19 pandemic was approximately 16,649.48 tons/day. Most publications agreed that the amount of waste has also increased due to the rapidly rising number of COVID-19 patients. In 15 articles, we identified 2 mentioning the COVID-19 biomedical waste on health. 9 out of 15 gave out the context related to the solution of BMW by COVID-19. More studies, including meta-analyses, are recommended to shed more light on the effects of medical waste on environmental health during the COVID-19 pandemic.

Viable medical waste chain network design by considering risk and robustness

Medical waste management (MWM) is an important and necessary problem in the COVID-19 situation for treatment staff. When the number of infectious patients grows up, the amount of MWMs increases day by day. We present medical waste chain network design (MWCND) that contains health center (HC), waste segregation (WS), waste purchase contractor (WPC), and landfill. We propose to locate WS to decrease waste and recover them and send them to the WPC. Recovering medical waste like metal and plastic can help the environment and return to the production cycle. Therefore, we proposed a novel viable MWCND by a novel two-stage robust stochastic programming that considers resiliency (flexibility and network complexity) and sustainable (energy and environment) requirements. Therefore, we try to consider risks by conditional value at risk (CVaR) and improve robustness and agility to demand fluctuation and network. We utilize and solve it by GAMS CPLEX solver. The results show that by increasing the conservative coefficient, the confidence level of CVaR and waste recovery coefficient increases cost function and population risk. Moreover, increasing demand and scale of the problem makes to increase the cost function.

Optimization of medical waste recycling network considering disposal capacity bottlenecks under a novel coronavirus pneumonia outbreak

The sudden outbreak and prolonged impact of the global novel coronavirus disease (COVID-19) epidemic has caused an increase in demand for medical products, such as masks and protective clothing, leading to an exponential increase in the generation of medical waste. As medical waste under the epidemic is highly infectious, it poses a great danger to human health. Therefore, with the proliferation of medical waste, it has become crucial to construct a reverse logistics recycling network that can handle medical waste quickly and efficiently. In this study, we construct a multi-period medical waste emergency reverse logistics network siting model with the objectives of minimum cost, minimum safety risk, and minimum time for the safe and quick disposal of medical waste. The model considers disposal capacity bottlenecks of existing facilities. Based on an empirical analysis using the COVID-19 epidemic in New York City, USA, as a case study, we find that the use of a suitable number of synergistic facilities and the establishment of temporary medical waste disposal centers are viable options for handling the dramatic increase in medical waste during the peak of the COVID-19 epidemic.

Optimization of production batches in a circular supply chain under uncertainty

During the covid-19 outbreak, millions of people are required to use disposable masks to protect themselves from this infectious disease. Under an exponential spread tendency of this pandemic, the conventional end of life of these plastic products is to dispose of them in landfills or to incinerate them. Actually, this scenario is the most optimistic one since millions of tonnes of masks are ended up in oceans. Therefore, recycling which is one of the circular economy concepts remains the key solution aiding countries to mitigate masks pollution, in addition to its role in decreasing the widespread of the disease. In this paper, we will exanimate several studies dealing with reverse logistics and circular economy, then we will develop a closed-loop supply chain design during the pandemic and translate it mathematically as a MILP model under certain and uncertain client demand. Finally, the problem will be solved using IBM ILOG Cplex Optimization Studio and the obtained results will be discussed.

Environmental policies for the treatment of waste generated by COVID-19: Text mining review

The rapid transmission of COVID-19 has meant that all economic and human efforts have been focused on confronting it, ignoring environmental aspects whose consequences are causing adverse situations all over the planet. The saturation of the sanitary system and confinement measures have multiplied the waste generated, which implies the need to adapt environmental policies to this new situation caused by the pandemic. It is a review article whose objective was to identify the environmental policies that would facilitate an adequate treatment of the waste generated by the pandemic. It was proposed to analyse the current lines of research developed on this paradigm, applying the text mining methodology. A systematic review of 111 studies published in environmental journals indexed in the Web of Science was carried out. The results identified three areas of interest: knowledge of transmission routes, management of the massive generation of plastics and appropriate treatment of solid waste in extreme situations. Leaders are called upon to implement the contingency plans to sustainably alleviate the enormous waste burden caused by society's adaptation to the restrictions imposed by the pandemic. Specifically, innovation aimed at achieving the reuse of medical products, the promotion of the circular economy and educational campaigns to promote clean environments should be encouraged.

Multi-purpose reverse logistics network design for medical waste management in a megacity: Istanbul, Turkey

In the study, a multi-purpose reverse logistics network has been designed to create effectual management of medical waste (MW) generated in 39 districts of Istanbul, a heavily populated city, during the COVID-19 pandemic as well as that to be generated in the next decade. With the model, the medical waste management system in Istanbul is analyzed during the pandemic and for the next 10 years. The model attempts to integrate economic, environmental, and social objectives within the sustainable development goals. It aims to maximize the number of personnel and government earnings for the estimated MW of a megacity while minimizing the total fixed cost and the cost of carbon emissions and transportation. The results indicated that the existing facilities are sufficient for the treatment and disposal of MW generated even under pandemic conditions. However, the capacity of the sterilization facility could be insufficient to treat the estimated amount of MW in the next decade. Opening a sterilization facility near the sanitary landfill in Komurcuoda with a total management cost of 62,450,332 €/year would be an optimum solution for Istanbul MW. In comparison to the single-purpose model results, the multi-purpose model resulted in approximately 42,000 € more in total cost. Sensitivity analyses show that the amount of MW has the most significant effect on the total cost. This simple model created an effective MW management proposal for Istanbul, which can be a model for megacities.

Sustainable development-oriented location-transportation integrated optimization problem regarding multi-period multi-type disaster medical waste during COVID-19 pandemic

After the outbreak of COVID-19 pandemic, devising an effective reverse logistics supply chain to clean up disaster medical waste is conducive to controlling and containing novel coronavirus transmission. Thus, the focus of this paper concentrates on multi-period multi-type disaster medical waste location-transportation integrated optimization problem with the concern of sustainability, which is formulated as a tri-objective mixed-integer programming model with the goals of maximizing total economic benefits, minimizing total carbon emissions and total potential social risks. Then, a real-world case from Wuhan using CPLEX solver is used to validate the developed model. Results indicate that constructing DMWTTSs with flexible capacity in different periods is encouraged to handle the sharply increasing disaster medical waste. The multi-period decision model outperforms the single-period one in disaster medical waste supply chains because the former has the capability of handling the uncertainties in the future periods. Increasingly, since the increase of budget doesn't always work well and social resources are limited, the estimation of minimum budget to obtain optimum overall performance is of great importance.

Collaborative Reverse Logistics Network for Infectious Medical Waste Management during the COVID-19 Outbreak

The development of COVID-19 in China has gradually become normalized; thus, the prevention and control of the pandemic has encountered new problems: the amount of infectious medical waste (IMW) has increased sharply; the location of outbreaks are highly unpredictable; and the pandemic occurs everywhere. Thus, it is vital to design an effective IMW reverse logistics network to cope with these problems. This paper firstly introduces mobile processing centers (MPCs) into an IMW reverse logistics network for resource-saving, quick response, and the sufficient capacity of processing centers. Then, a multi-participant-based (public central hospitals, disposal institutions, the logistics providers, and the government) collaborative location and a routing optimization model for IMW reverse logistics are built from an economic, environmental perspective. An augmented ε-constraint method is developed to solve this proposed model. Through a case study in Chongqing, it is found that for uncertain outbreak situations, fixed processing centers (FPCs) and MPCs can form better disposal strategies. MPC can expand the processing capacity flexibly in response to the sudden increase in IMW. The results demonstrate good performance in reduction in cost and infection risk, which could greatly support the decision making of IMW management for the government in the pandemic prevention and control.

Current scenario of solid waste management techniques and challenges in Covid-19 - A review

Annually, world generates 2.01 billion tonnes of solid wastes and it is expected to generate 2.2 billion tonnes of solid waste by 2025. Globally double the amount of waste generation was anticipated by 2050, hence an urgent action is required for this intricate problem in adopting better management techniques and recycling strategies. Unfortunately, poor management of wastes causes vulnerable effects to the society in terms of health. Waste management is the key infrastructure to be developed in society, but so far it is not recognized as much in many developing countries. Significant innovations and improvements are made in the last few decades globally, but still 2 to 3 billion people around the world lack access to waste collection services. The aim of this present study is to give an overview of different types of waste techniques that are effectively followed by different countries and the action plans need to follow. This review focuses on the global current scenario of waste generation, and its management methods with relevant literatures providing the upgrades in the phases of waste management services like collection and transport, various techniques adopted for waste management, policies and legislation, countries investment in waste management process and the impact of solid waste management during Covid-19. Collectively we conclude that Asian countries need to allot more fund for handling solid waste. Also with the available waste management technique, it is not possible to achieve zero waste. Therefore, more new techniques are needed to be adapted.

Agricultural biomass supply chain resilience: COVID-19 outbreak vs. sustainability compliance, technological change, uncertainties, and policies

The COVID-19 pandemic has caused a confounding collection of transportation, supply chains, and logistical disruptions, which needs to be well addressed by businesses and governments. During this pandemic, several researchers have concentrated on the sustainability and resilience of supply chains in various industry sectors. Nevertheless, the impacts of the pandemic on sustainability pillars, technological change and uncertainties, and resilience approaches in various sectors have not been clarified yet. More specifically, the agricultural biomass sector has experienced serious disruptions induced by the COVID-19. This paper aims to analyze and assess the agricultural biomass supply and production systems during the COVID-19 and their recovery in post-COVID-19. To the best of our knowledge, this is the first study to evaluate the economic, environmental, social, and technological change effects of the COVID-19 on Biomass Supply Chain resilience. Uncertainties of oil and palm energy demand, price, consumption, export, and production of leading producers and suppliers worldwide are analyzed considering the pre-COVID-19 and current COVID-19 period. It is then followed by recommendations for specific strategies, policies for the biomass industry, and general action plan to overcome the problems of resilience in medium, short, and long-term horizon plans. The findings from this study are valuable resources for the governments, biomass industry, and other stakeholders to provide cleaner post-pandemic energy production and supply and enhance the employees’ engagement.

Locating temporary waste treatment facilities in the cities to handle the explosive growth of HCWs during pandemics: A novel Grey-AHP-OCRA hybrid approach

The COVID-19 outbreak has not only put the community health at stake but, also the environmental health. Usually, the healthcare wastes (HCWs) are composed of 15-20% of the infectious wastes and the rest of the non-infectious wastes. But, during any communicable health outbreak like COVID-19, the whole HCWs coming from the infected people become contagious. During the COVID-19 outbreak, the infectious waste is not only limited to the hospitals' premises, but also comes from the households, where COVID19 infected people are under home quarantine. Hence, keeping in mind the explosive growth in generation rates of infectious HCWs, the present study targets to expand the treatment and disposal capacity by installing temporary healthcare waste treatment facilities (HCWTFs). The study identifies ten criteria from the literature review and in consultation with the field experts, to evaluate the potential candidates for setting up temporary HCWTF during the health outbreaks. The study proposes a hybrid methodology based on grey analytical hierarchy process (G-AHP) and grey operational competitiveness rating analysis (grey-OCRA) for prioritizing the evaluation criteria and selecting the optimal temporary HCWTF location by considering the experts' inputs, respectively. The stakeholders consider the 'proximity to the inhabitation', 'infrastructure availability', and 'transportation distance' are the most important criteria for selecting the temporary HCWTF location. The proposed methodology is applied to select the temporary HCWTF location in Sundargarh District, Odisha, India. The study identifies the four locations by using geographical information system (GIS) tools and sequences them as per the preferences given by the stakeholders on various identified criteria. The study may be useful for the administration to set up the temporary facilities to quickly dispose of the extra HCWs during the pandemics. However, the future studies can be targeted to coordinate the collection, storage and transportation activities with the temporary HCWTFs.

Collaborative Routing Optimization Model for Reverse Logistics of Construction and Demolition Waste from Sustainable Perspective

The construction industry is developing rapidly along with the acceleration of urbanization but accompanied by an increased amount of construction and demolition waste (CDW). From the perspective of sustainability, the existing research has mainly focused on CDW treatment or landfill disposal, but the challenge of reverse logistics of CDW recycling that provides overall CDW route planning for multiple participants and coordinates the transportation process between multiple participants is still unclear. This paper develops an optimization model for multi-depot vehicle routing problems with time windows (MDVRPTW) for CDW transportation that is capable of coordinating involved CDW participants and suggesting a cost-effective, environment-friendly, and resource-saving transportation plan. Firstly, economic cost, environmental pollution, and social impact are discussed to establish this optimization-oriented decision model for MDVRPTW. Then, a method combined with a large neighborhood search algorithm and a local search algorithm is developed to plan the transportation route for CDW reverse logistics process. With the numerical experiments, the computational results illustrate the better performance of this proposed method than those traditional methods such as adaptive large neighborhood search algorithm or adaptive genetic algorithm. Finally, a sensitivity analysis considering time window, vehicle capacity, and carbon tax rate is conducted respectively, which provides management implications to support the decision-making of resource utilization maximization for enterprises and carbon emission management for the government.

An equitable and accessible vaccine supply chain network in the epidemic outbreak of COVID-19 under uncertainty

Vaccination is one of the most efficient ways to restrict and control the spread of epidemic outbreaks such as COVID-19. Due to the limited COVID-19 vaccine supply, an equitable and accessible plan should be prepared to cope with. This research focuses on designing a vaccine supply chain while aiming to achieve an equitable and accessible network. We present a novel mathematical formulation that helps to optimize vaccine distribution to inoculate people with various priority levels to achieve an equitable plan. The transshipment strategy is also incorporated into the model to enhance the accessibility of COVID-19 vaccine types between health facilities. The nature of COVID-19 is dynamic over time due to mutations, and the protection level of each vaccine type against this disease is not exact. Besides, complete information about the demand for different vaccine types is not available. Hence, we use Multi-Stage Stochastic Programming as a reliable strategy that is organized to manage stochastic data in a dynamic environment for the first time in the vaccine supply chain network. The scenarios in this approach are generated using a Monte Carlo simulation method, and then a forward scenario reduction technique is conducted to construct a suitable scenario tree. The practicality and capability of the model are shown in a real-life case of Iran. The results show that the performance of the Multi-Stage Stochastic Programming is significantly improved compared with the two-stage stochastic programming regarding the total cost of the vaccine supply chain and the number of the shortage units.

Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: artificial intelligence-based solutions

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence.

Evolutionary Game Analysis of Medical Waste Disposal in China under Different Reward and Penalty Models

Although local governments have issued relevant reward and penalty policies, there are still problems of medical waste disposal in China, particularly in light of the special situation of the COVID-19 pandemic. Furthermore, these problems are generated in the game between local governments and disposal enterprises. Accordingly, based on the evolutionary game theory, this paper establishes and analyzes the game system between local governments and disposal enterprises under four modes: static reward and static penalty, dynamic reward and static penalty, static reward and dynamic penalty, and dynamic reward and dynamic penalty. The theoretical analysis is verified through numerical simulation of a medical waste disposal case in China. The results showed that when local governments choose the static reward and static penalty mode, the game system hardly always has an evolutionary stable state, and the dynamic reward or dynamic penalty mode can make up for the shortcomings of the static reward and static penalty mode. The static reward and dynamic penalty mode is considerably better than the other two dynamic reward and penalty modes, which has the best effect on improving the quality of medical waste disposal. Additionally, if the reward or penalty increases dynamically, local governments tend to implement a “relaxed supervision” strategy, and disposal enterprises will still improve the disposal quality of medical waste. The suggestions proposed based on the research conclusions offer some enlightenment for policymakers to formulate reasonable reward and penalty measures.

A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item

The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.

Cuckoo optimization algorithm in reverse logistics: A network design for COVID-19 waste management

Today, reverse logistics (RL) is one of the main activities of supply chain management that covers all physical activities associated with return products (such as collection, recovery, recycling and destruction). In this regard, the designing and proper implementation of RL, in addition to increasing the level of customer satisfaction, reduces inventory and transportation costs. In this paper, in order to minimize the costs associated with fixed costs, material flow costs, and the costs of building potential centres, a complex integer linear programming model for an integrated direct logistics and RL network design is presented. Due to the outbreak of the ongoing global coronavirus pandemic (COVID-19) at the beginning of 2020 and the consequent increase in medical waste, the need for an inverse logistics system to manage waste is strongly felt. Also, due to the worldwide vaccination in the near future, this waste will increase even more and careful management must be done in this regard. For this purpose, the proposed RL model in the field of COVID-19 waste management and especially vaccine waste has been designed. The network consists of three parts - factory, consumers' and recycling centres - each of which has different sub-parts. Finally, the proposed model is solved using the cuckoo optimization algorithm, which is one of the newest and most powerful meta-heuristic algorithms, and the computational results are presented along with its sensitivity analysis.

COVID-19 and urban rivers: Effects of lockdown period on surface water pollution and quality- A case study of the Zarjoub River, north of Iran

Due to the spreading of the coronavirus (COVID-19) in Iran, restrictions and lockdown were announced to control the infection. In order to determine the effects of the lockdown period on the status of the water quality and pollution, the concentrations of Al, As, Ba, Cr, Cu, Mo, Ni, Pb, Se, and Zn, together with Na+, Mg2+, Ca2+ and electrical conductivity (EC), were measured in the Zarjoub River, north of Iran, in both pre-lockdown and post-lockdown periods. The results indicated that water pollution and associated human health risk reduced by an average of 30% and 39%, respectively, during the lockdown period. In addition, the multi-purpose water quality index also improved by an average of 34%. However, the water salinity and alkalinity increased during the lockdown period due to the increase of municipal wastewater and the use of disinfectants. The major sources of pollution were identified as weathering, municipal wastewater, industrial and agricultural effluents, solid waste, and vehicular pollution. PCA-MLR receptor model showed that the contribution of mixed sources of weathering and municipal wastewater in water pollution increased from 23 to 50% during the lockdown period. However, the contribution of mixed sources of industrial effluents and solid wastes reduced from 64 to 45%. Likewise, the contribution of traffic-related sources exhibited a reduction from 13% in the pre-lockdown period to 5% together with agricultural effluent in the post-lockdown period. Overall, although the lockdown period resulted in positive impacts on diminishing the level of water pollution caused by industrial and vehicular contaminants, the increase of municipal waste and wastewater is a negative consequence of the lockdown period.

A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions

Industrialization and population growth have been accompanied by many problems such as waste management worldwide. Waste management and reduction have a vital role in national management. The presents study represents a multi-objective location-routing problem for hazardous wastes. The model was solved using Non dominated Sorting Genetic Algorithm-II, Multi-Objective Particle Swarm Optimization, Multi-Objective Invasive Weed Optimization, Pareto Envelope-based Selection Algorithm, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer algorithms. The findings revealed that the Multi-Objective Invasive Weed Optimization algorithm was the best and the most efficient among the algorithms used in this study. Obtaining income from the incineration of the wastes and reducing the risk of COVID-19 infection are the first innovation of the present study, which considered in the presented model. The second innovation is that uncertainty was considered for some of the crucial parameters of the model while the robust fuzzy optimization model was applied. Besides, the model was solved using several meta-heuristic algorithms such as Multi-Objective Invasive Weed Optimization, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer, which were rarely used in literature. Eventually, the most efficient algorithm was identified by comparing the considered algorithms.

Parameter setting of meta-heuristic algorithms: a new hybrid method based on DEA and RSM

The parameter setting of meta-heuristic algorithms is one of the most effective issues in the performance of meta-heuristic algorithms and is usually done experimentally which is very time-consuming. In this research, a new hybrid method for selecting the optimal parameters of meta-heuristic algorithms is presented. The proposed method is a combination of data envelopment analysis method and response surface methodology, called DSM. In addition to optimizing parameters, it also simultaneously maximizes efficiency. In this research, the hybrid DSM method has been used to set the parameters of the cuckoo optimization algorithm to optimize the standard and experimental functions of Ackley and Rastrigin. In addition to standard functions, in order to evaluate the performance of the proposed method in real problems, the parameter of reverse logistics problem for COVID-19 waste management has been adjusted using the DSM method, and the results show better performance of the DSM method in terms of solution time, number of iterations, efficiency, and accuracy of the objective function compared to other.

A comprehensive review of the application of plasma gasification technology in circumventing the medical waste in a post-COVID-19 scenario

The recent COVID-19 pandemic, which has hit the world, is third in the last two decades. The safety and precaution measures have led to the generation of a colossal pile of biomedical waste, including plastic waste, due to the usage of personal protective equipment kits and safety equipment that is not easily manageable. The environment and health and safety concerns for humans require biomedical waste to be treated with an outstanding treatment process that can help humanity manage it by adhering to strict environmental norms prescribed. The plasma gasification technology is the most beneficial and efficient technology for treating biomedical waste. The byproducts generated can be utilized further as valuable inputs in other industries, thus strengthening the circular economy concept. In this research paper, the applicability of plasma gasification for the treatment of biomedical waste in the present scenario has been reviewed. The feasibility and applicability of the technology in handling biomedical waste have been reviewed via various research articles in this study. Also, further steps have been suggested for the Indian scenario to make this technology commercially viable in the long run.

GRAPHICAL ABSTRACT

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

This study develops a novel mathematical model to design a sustainable mask Closed-Loop Supply Chain Network (CLSCN) during the COVID-19 outbreak for the first time. A multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to address the locational, supply, production, distribution, collection, quarantine, recycling, reuse, and disposal decisions within a multi-period multi-echelon multi-product supply chain. Additionally, sustainable development is studied in terms of minimizing the total cost, total pollution and total human risk at the same time. Since the CLSCN design is an NP-hard problem, Multi-Objective Grey Wolf Optimization (MOGWO) algorithm and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are implemented to solve the proposed model and to find Pareto optimal solutions. Since Meta-heuristic algorithms are sensitive to their input parameters, the Taguchi design method is applied to tune and control the parameters. Then, a comparison is performed using four assessment metrics including Max-Spread, Spread of Non-Dominance Solution (SNS), Number of Pareto Solutions (NPS), and Mean Ideal Distance (MID). Additionally, a statistical test is employed to evaluate the quality of the obtained Pareto frontier by the presented algorithms. The obtained results reveal that the MOGWO algorithm is more reliable to tackle the problem such that it is about 25% superior to NSGA-II in terms of the dispersion of Pareto solutions and about 2% superior in terms of the solution quality. To validate the proposed mathematical model and testing its applicability, a real case study in Tehran/Iran is investigated as well as a set of sensitivity analyses on important parameters. Finally, the practical implications are discussed and useful managerial insights are given.

Mapping healthcare waste management research: Past evolution, current challenges, and future perspectives towards a circular economy transition

Improper healthcare waste (HCW) management poses significant risks to the environment, human health, and socio-economic sustainability due to the infectious and hazardous nature of HCW. This research aims at rendering a comprehensive landscape of the body of research on HCW management by (i) mapping the scientific development of HCW research, (ii) identifying the prominent HCW research themes and trends, and (iii) providing a research agenda for HCW management towards a circular economy (CE) transition and sustainable environment. The analysis revealed four dominant HCW research themes: (1) HCW minimization, sustainable management, and policy-making; (2) HCW incineration and its associated environmental impacts; (3) hazardous HCW management practices; and (4) HCW handling and occupational safety and training. The results showed that the healthcare industry, despite its potential to contribute to the CE transition, has been overlooked in the CE discourse due to the single-use mindset of the healthcare industry in the wake of the infectious, toxic, and hazardous nature of HCW streams. The findings shed light on the HCW management domain by uncovering the current status of HCW research, highlighting the existing gaps and challenges, and providing potential avenues for further research towards a CE transition in the healthcare industry and HCW management.

Recovery solutions for ecotourism centers during the Covid-19 pandemic: Utilizing Fuzzy DEMATEL and Fuzzy VIKOR methods

Obviously, the Covid-19 pandemic has huge impact on most businesses and has caused serious and countless problems for them. Therefore, providing solutions for affected businesses to recover and improve their activities during pandemic times is inevitable. In this regard, ecotourism centers are one of the businesses that went through this problem and have faced significant dilemmas in their activities. Also, reportedly, there is no related research focusing on the recovery approaches to address these obstacles relating to these kinds of businesses during the pandemic. Therefore, all of these exhorted us to do the current research. In this paper, some practical and useful action plans for ecotourism centers are firstly developed to help these businesses. To obtain the action plans, some brainstorming sessions were held consisting of tourism experts, university professors, managers, owners, and some personnel of eco-tourism centers. In order to prioritize the defined action plans, four criteria are considered. Firstly, we compute the weights of the considered criteria by the Fuzzy DEMATEL and then they are prioritized using the Fuzzy VIKOR. The findings of the current study divulge that the AP2 "Standardization of the centers" and AP3 "Estimating demand number and increasing the capacity" and AP7 "Identifying other natural tourist attractions of the region" have the highest and lowest priority to be implemented.

Medical Waste Treatment Technologies for Energy, Fuels, and Materials Production: A Review

The importance of medical waste management has grown during the COVID-19 pandemic because of the increase in medical waste quantity and the significant dangers of these highly infected wastes for human health and the environment. This innovative review focuses on the possibility of materials, gas/liquid/solid fuels, thermal energy, and electric power production from medical waste fractions. Appropriate and promising treatment/disposal technologies, such as (i) acid hydrolysis, (ii) acid/enzymatic hydrolysis, (iii) anaerobic digestion, (vi) autoclaving, (v) enzymatic oxidation, (vi) hydrothermal carbonization/treatment, (vii) incineration/steam heat recovery system, (viii) pyrolysis/Rankine cycle, (ix) rotary kiln treatment, (x) microwave/steam sterilization, (xi) plasma gasification/melting, (xii) sulfonation, (xiii) batch reactor thermal cracking, and (xiv) torrefaction, were investigated. The medical waste generation data were collected according to numerous researchers from various countries, and divided into gross medical waste and hazardous medical waste. Moreover, the medical wastes were separated into categories and types according to the international literature and the medical waste fractions’ percentages were estimated. The capability of the examined medical waste treatment technologies to produce energy, fuels, and materials, and eliminate the medical waste management problem, was very promising with regard to the near future.

Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model

Municipal solid waste (MSW) directly impacts community health and environmental degradation; therefore, the management of MSW is crucial. Medical waste is a specific type of MSW which is generally divided into two categories: infectious and non-infectious. Wastes generated by coronavirus disease 2019 (COVID-19) are classified among infectious medical wastes; moreover, these wastes are hazardous because they threaten the environment and living organisms if they are not appropriately managed. This paper develops a bi-objective mixed-integer linear programming model for medical waste management during the COVID-19 outbreak. The proposed model minimizes the total costs and risks, simultaneously, of the population's exposure to pollution. This paper considers some realistic assumptions for the first time, including location-routing problem, time window-based green vehicle routing problem, vehicles scheduling, vehicles failure, split delivery, population risk, and load-dependent fuel consumption to manage both infectious and non-infectious medical waste. We apply a fuzzy goal programming approach for solving the proposed bi-objective model, and the efficiency of the proposed model and solution approach is assessed using data related to 13 nodes of medical waste production in a location west of Tehran.

How to Dispose of Medical Waste Caused by COVID-19? A Case Study of China

The rapid increase in novel coronavirus (COVID-19) patients also means a rapid increase in medical waste that could carry the novel coronavirus (SARS-CoV-2). How to safely dispose of medical waste caused by COVID-19 is a huge challenge that needs to be solved urgently. The outbreak of the COVID-19 has led to a significant increase in the daily generation of medical waste in China and has placed a severe test on the Chinese medical waste disposal system. Unlike ordinary wastes and garbage, medical waste that is untreated or incompletely treated will not only cause environmental pollution, but also directly or indirectly cause infections and endanger people's health. Faced with difficulties, the Chinese government formulated a policy for medical waste management and a response plan for the epidemic, which provides policy guarantee for the standardized disposal of epidemic medical waste. In addition, the government and medical institutions at all levels formed a comprehensive, refined, and standardized medical treatment process system during research and practice. China has increased the capacity of medical waste disposal in various places by constructing new centralized disposal centers and adding mobile disposal facilities. China has achieved good results in the fight against COVID-19, and the pressure on medical waste disposal has been relieved to a certain extent. However, the global epidemic situation is severe. How to ensure the proper and safe disposal of medical waste is related to the prevention and control of the epidemic situation. This study summarizes China's experience in the disposal of medical waste in the special case of COVID-19 and hopes to provide some reference for other countries in the disposal of medical waste.

Designing a testing kit supply network for suspected COVID-19 cases under mixed uncertainty approach

Motivated by the COVID-19 (C-19) pandemic and the challenges it poses to global health and the medical communities, this research aims to investigate the factors affecting of reduction health inequalities related to the C-19 to tackle the increasing number of outbreaks and their social consequences in such a pandemic. Hence, we design a COVID-19 testing kit supply network (C-19TKSN) to allocate various C-19 test kits to the suspected C-19 cases depending on the time between the emergence of their first symptoms and the time they are tested. In particular, this model aims to minimize the total network cost and decrease false results C-19 test by considering the fundamental characteristics of a diagnostic C-19 test (i.e., specificity and sensitivity). In the sensitivity characteristic, a gamma formula is presented to estimate the error rate of false-negative results. The nature of the C-19TKSN problem is dynamic over time due to difficult predictions and changes in the number of C-19 patients. For this reason, we consider the potential demands relating to different regions of the suspected C-19 cases for various C-19 test kits and the rate of prevalence of C-19 as stochastic parameters. Accordingly, a multi-stage stochastic programming (MSSP) method with a combined scenario tree is proposed to deal with the stochastic data in a dynamic environment. Then, a fuzzy approach is employed based on M e measure to cope with the epistemic uncertainty of input data. Eventually, the practicality and capability of the proposed model are shown in a real-life case in Iran. The results demonstrate that the performance of the MSSP model is significantly better in comparison with the two-stage stochastic programming (TSSP) model regarding the false results and the total cost of the network.

Development of sustainable and resilient healthcare and non-cold pharmaceutical distribution supply chain for COVID-19 pandemic: a case study

Purpose

This study evaluated the influence of the coronavirus pandemic on the healthcare and non-cold pharmaceutical care distribution supply chain.

Design/methodology/approach

The model involves four objective functions to minimize the total costs, environmental impacts, lead time and the probability of a healthcare provider being infected by a sick person was developed. An improved version of the augmented e-constraint method was applied to solve the proposed model for a case study of a distribution company to show the effectiveness of the proposed model. A sensitivity analysis was conducted to identify the sensitive parameters. Finally, two robust models were developed to overcome the innate uncertainty of sensitive parameters.

Findings

The result demonstrated a significant reduction in total costs, environmental impacts, lead time and probability of a healthcare worker being infected from a sick person by 40%, 30%, 75% and 54%, respectively, under the coronavirus pandemic compared to the normal condition. It should be noted that decreasing lead time and disease infection rate could reduce mortality and promote the model's effectiveness.

Practical implications

Implementing this model could assist the healthcare and pharmaceutical distributors to make more informed decisions to minimize the cost, lead time, environmental impacts and enhance their supply chain resiliency.

Originality/value

This study introduced an objective function to consider the coronavirus infection rates among the healthcare workers impacted by the pharmaceutical/healthcare products supply chain. This study considered both economic and environmental consequences caused by the coronavirus pandemic condition, which occurred on a significantly larger scale than past pandemic and epidemic crises.

Medical waste management during coronavirus disease 2019 pandemic at the city level

Coronavirus disease 2019 brings about the economic damage and loss of life. Thus, demand of personal protective equipment continues to increase, consequently an increase in infectious equipment pollution. Most of these wastes threaten the environment and increase the spread of diseases. This paper provides a research hypothesis whether effective medical waste management would prevent the possible impacts of coronavirus disease 2019-related waste issues on environment at the city level. To confirm this hypothesis, installation of waste treatment centre is addressed. Then, by incorporating uncertain waste generation amounts utilizing Jimenez method, a pickup routing is addressed to decide the pickup routes between the waste treatment centre and residential area. This study is first to assign the optimistic, realistic and pessimistic scenarios of the uncertain waste generation using time series analysis method and waste generation formulation. Also, L-type matrix is used to define, assess and prioritize the environmental and operational risks on waste generation formulation and to provide risk reaction strategies. Practicality of these approaches is illustrated in the case of Turkey. The computational results reveal the effectiveness of the integrated method, which ensures practical and theoretical insights controlling the waste generation to prevent virus propagation for health authorities.