Next Article in Journal
Forecasting Future Vegetation Dynamics under SSP/RCP Pathways under Spatially Changing Climate and Human Activities Conditions
Next Article in Special Issue
Technology-Organization-External-Sustainability (TOES) Framework for Technology Adoption: Critical Analysis of Models for Industry 4.0 Implementation Projects
Previous Article in Journal
Reviewing the Roles of AI-Integrated Technologies in Sustainable Supply Chain Management: Research Propositions and a Framework for Future Directions
Previous Article in Special Issue
The Procurement 4.0 Contributions to Circular Economy
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 14
10.3390/su16146187
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

Simulation of Hospital Waste Supply Chain in the Context of Industry 4.0—A Systematic Literature Review

by
André Ferreira
1,
Ana L. Ramos
1,
José V. Ferreira
1 and
Luís P. Ferreira
2,3,*
1
GOVCOPP, DEGEIT, University of Aveiro, 3810-193 Aveiro, Portugal
2
School of Engineering, ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, Portugal
3
Associate Laboratory for Energy, Transports and Aerospace (LAETA-INEGI), Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(14), 6187; https://doi.org/10.3390/su16146187
Submission received: 5 June 2024 / Revised: 4 July 2024 / Accepted: 16 July 2024 / Published: 19 July 2024
(This article belongs to the Special Issue Industry 4.0 Technologies for Sustainable Manufacturing and Supply Chain Operations)
Browse Figures
Versions Notes

Abstract

:
Supply chain management is a crucial task for all companies, as it is essential to respond to increased demand and competitiveness. An exponential increase in hospital waste can cause very significant risks to both humans and the environment if it is not treated adequately. In this way, the efficient management of this waste can bring benefits and prevent an increase in the likelihood of disease occurring. With the growth of technology, there are various tools available to improve decision-making in the supply chain. Simulation, one of the pillars of Industry 4.0, is one of the tools being used. With the complexity of systems increasing, hybrid simulation provides more precise solutions to problems. The aim of this article is therefore to systematically research and analyze the literature on the use of simulation/hybrid simulation in supply chains in the health sector. In this way, 20 articles that approach simulation as a decision support tool in hospital waste supply chains were analyzed in the context of Industry 4.0. The results indicate that the years 2020 and 2023 had the highest number of publications (with five each) and that countries such as China and Indonesia had the highest number of documents on this topic (with five each). The topics in this research area with more related documents were environmental sciences, computer sciences and medicine. This research highlights the fundamental role of simulation approaches in improving the performance of supply chains in the healthcare sector, promoting resilience and improving healthcare outcomes for patients.

1. Introduction

The increase in population has led to an expansion in global spending on healthcare, making it one of the fastest growing industries in recent years [1,2]. The health sector has grown exponentially due to increased public awareness and globalization [3]. Hospitals, nursing homes and various pathology laboratories have contributed to the increase in hospital waste, which includes syringes, scalpels, bloody cottons and chemicals, leading the World Health Organization to consider this type of waste a non-hazardous municipal solid waste [4]. Medical waste has increased with the increase in the world’s ageing population, but also with the emergence of the COVID-19 pandemic [5]. It is estimated that the amount of medical waste generated by the COVID-19 pandemic was 2.6 million tons per day worldwide [6]. Medical waste includes all solid and liquid waste produced in the treatment, diagnosis and immunization phases of both animals and humans [7]. Hospital waste is the second most dangerous type of waste in the world, and second only to radiation waste [8]. Medical waste contains a wide variety of microbial contaminants and chemical compounds, making it essential to take particular care to increase prevention and reduce the transmission of diseases and viruses [9]. The inappropriate use and disposal of hospital waste is known to be a source of preventable infection and demonstrates low levels of management [10]. It is projected that healthcare waste management costs will grow by 5.3% annually (USD 11.77 billion in 2018 to USD 17.89 billion in 2026) [11]. Although health services increase people’s life expectancy through the treatment and prevention of diseases, they generate a large amount of medical waste [12].
Therefore, due to the consequences of the improper use and disposal of medical waste, it is necessary to take additional care when it comes to managing a hospital waste supply chain. As a result, with advances in technology, there are tools that make it possible to analyze waste more carefully, such as simulation, which makes it possible to create various scenarios to ascertain which is the best for decision-making, thus reducing the risk of infection.
The objective of this article is to carry out a systematic search of the literature and a bibliometric analysis that studies and analyzes similar works on the subject of hospital waste supply chain management through simulation.
This article is divided into six sections. Section 1 presents a brief overview of the central themes and aim of the article. Section 2 presents a literature review of the topics. Section 3 describes the methodology used to conduct this study. Section 4 contains the results of an analysis of the studies chosen according to the criteria. Section 5 discusses further themes. Finally, Section 6 provides some limitations about the work and also presents the final conclusions.

2. Literature Review

2.1. Industry 4.0

The need to constantly adapt and use new technologies has forced industry to evolve into a new era, giving rise to a new industry: Industry 4.0 [13]. The concept of Industry 4.0 was mentioned by the German government’s initiative in 2011 as a strategy for production in the country’s industrial sector [14]. The main goal of Industry 4.0 is to improve the operational performance, efficiency and effectiveness of a given company by digitizing and connecting machines and equipment [15]. The objective of Industry 4.0 is to transform machines into self-aware machines in order to increase overall performance [16].
The implementation of Industry 4.0 enables both the horizontal and vertical connectivity of people, equipment and objects based on the Internet in real time, making it possible to solve the various challenges that companies are currently experiencing [17]. Industry 4.0 makes it possible to connect industry through an internal and external supply chain network in a digital way [14].
Industry 4.0 makes it possible to develop and improve the competitiveness of companies by producing a finished product that is of higher quality, is more comfortable, and is cheaper to use and maintain [18]. One of the fundamental principles of Industry 4.0 is the interconnection between processes and machines so that data can be collected, exchanged and analyzed to prevent future behavior, as well as to solve problems [19].

2.2. Healthcare Waste Management

The management of medical waste is one of the most complex and demanding tasks facing managers as the population grows and the demand for medical services rises [20]. The poor management of healthcare waste has negative effects on the environment and public health [21,22]. Waste management is a complex process consisting of the collection of medical waste, the identification of the transportation route, the location of the disposal site, the selection of the treatment technology and, finally, the recovery of valuable energy [23].
The main objective of medical waste management is to manage, reduce and eliminate biological and chemical problems [24]. Effective supply chain management is a challenge in the various sectors of a given organization; however, the complexity of managing a hospital supply chain is greater and requires more attention as it directly influences the safety and health of those involved [25]. Poor medical waste management can lead to the transmission of AIDS, hepatitis, typhoid fever and many other infectious diseases [26].
With an increase in spending on the treatment of medical waste, medical organizations have attempted to minimize costs and maintain good-quality medical services and dispose of infectious waste legally by selecting qualified companies for the disposal of medical waste [27]. Advances in technology, transportation and trade have allowed for global economic growth, also increasing the demand for medical equipment, but have nevertheless led to a gradual increase in poor medical waste management and unsafe disposal [11].
Waste management depends on (i) the disposal facilities; (ii) monitoring; (iii) collection; (iv) transportation and (v) treatment, but also on (vi) the behavior and capacity of human resources [28].
A sustainable supply chain model that allows problems to be addressed must be considered at the same time as reducing environmental damage [29].

2.3. Hybrid Simulation

Simulation allows for the better planning of operations through knowledge and more precise information about the system, making it possible to improve or maintain the competitiveness of companies [30]. By using simulation, it is possible to improve the quality of decision-making in an easier and faster way [16].
Computer simulation has been used as a way of evaluating complex system designs to determine whether they meet specific operational objectives, determining which is best for making the most appropriate decisions, thus saving time and resources [31]. Computer simulation modeling is a useful tool in various situations, such as modeling more complex systems and communicating ideas and validating analytical models [32].
In order to study the problem from various perspectives, hybrid simulation allows for a better understanding of more complex systems, with multiple objectives, levels of aggregation and behaviors [33]. The concept of “hybrid simulation” is not consensual, in that it can encompass models that are implemented simultaneously on analogue and digital computers, models that have continuous and discrete variables, but also models that combine simulation with an analytical method such as optimization [34].

2.4. Hospital Supply Chain in the Context of Industry 4.0

The health sector has existed since the dawn of civilization and with the increase in the number of people and technological facilities, it is crucial to provide the health service with better facilities and flexibility through Healthcare 4.0, which has developed from Industry 4.0 [35].
Technologies related to Industry 4.0 such as the Internet of Things (IoT), Big Data Analytics and Cloud Computing have contributed to the advancement of technology related to the health sector [36]. Digital technologies in the healthcare sector can attenuate or even eliminate the high costs faced by healthcare systems, which account for 10% of gross domestic product in the European Union and 18% in the United States [37].
Due to the large amount of data in the health sector, the use of Big Data can improve the quality of the health service, since the quality of the health service depends on the volume, quality and correct interpretation of patient data [36]. The Internet of Things enables remote monitoring that facilitates the sending and receiving of information between patients and healthcare facilities, and is therefore a key element of Healthcare 4.0, leading to remarkable changes in the healthcare sector [38]. The Internet of Things plays a significant role in the collection and monitoring of data [39].
Other technologies used in Industry 4.0 include artificial intelligence, which is capable of detecting any problems a patient may have through the use of sensors in an emergency situation, which is proving to be a beneficial contribution to the health sector [38]. Artificial intelligence is responsible for analyzing large amounts of data and making decisions based on learning from the data being analyzed [39].

3. Materials and Methods

The main objective of this work is to systematically analyze the literature on hybrid simulation in hospital waste supply chains in the context of Industry 4.0. A systematic literature review is a process that allows the collection of scientific publications on a given topic that can answer the research questions posed and that fit certain selection criteria [40]. Bibliometric analysis is a methodology that explores and analyzes substantial amounts of scientific data and whose importance has increased in recent years due to the advancement and availability of bibliometric software, as well as the interaction between different areas of bibliometric methodology [41].
The guidelines for performing a systematic literature review, namely PRISMA 2020, were therefore implemented as a methodology. The knowledge of the PRISMA 2020 statement is important when it comes to planning reviews so that as much information as possible is available [42].
This systematic literature review is designed to answer two research questions:
RQ1. What are the main difficulties in hospital waste supply chains?
RQ2. What contribution does simulation/hybrid simulation make to hospital waste supply chains?
Figure 1 provides an illustration of the methodology used throughout this research, which has allowed the above-mentioned research questions to be clarified.

Study Search

This systematic literature review used two databases, specifically SCOPUS and ScienceDirect, in order to collect as many articles as possible related to these concepts. The search was conducted on 6 May 2024. The following keywords were used to search for articles in the two databases in the “Title, abstract or author-specified keywords” search field: (“medical waste supply chain” OR “healthcare waste supply chain” OR “medical waste” OR “healthcare waste”) AND (“simulation” OR ‘Industry 4.0’). Through these conditions, 108 articles were found.
Table 1 presents the number of documents in the two databases under analysis.
Based on the results, it appears that the database with the most articles related to the subject of this systematic literature search is the Scopus database.
The inclusion criteria used in this paper were as follows: all existing documents since 2010. Then, duplicate articles were removed; (i) articles not written in English and (ii) only vaguely related to the theme were the exclusion criteria chosen for this research.
Table 2 summarizes the inclusion and exclusion criteria used for the document search.
The database search with these criteria resulted in 118 articles (97 in Scopus and 21 in ScienceDirect). Then, 15 repeated publications were removed, resulting in 103 documents. This was followed by the removal of six non-English documents and 77 other articles that were not suitable for the purpose of this research. The final result of the search was 20 articles since 2010. Figure 2 summarizes the results and the number of publications at each phase.

4. Results

The inclusion and exclusion criteria led to the selection of 20 documents. In this section, the documents will be analyzed from various aspects, such as the number of publications per year, the type of document, the number of publications per journal, the country or territory from which the document originated, the themes per document and the occurrence of keywords. Table 3 provides a summary of the general information about the selected documents.

4.1. Publications by Year

The number of publications in general has increased in recent years, with the highest number of articles published in 2020 and 2023 (both with 5 articles). It should also be noted that there were no articles published on this topic in 2019. Figure 3 illustrates the evolution of the number of articles published per year on the subject.
Figure 3 indicates that the number of articles published on this subject could increase even further. There is also a difference in the publication figures between the years 2016 and 2017 and the years from 2020 onwards.

4.2. Documents by Type

Following this, the types of document obtained were analyzed. Of the 20 documents, 30% were conference documents, while the remaining 70% were articles. This is analyzed in Figure 4.

4.3. Journals

With regard to the journal in which the studies were published, the International Journal of Environmental Research and Public Health had the highest number of publications (three publications). The remaining journals had one publication. Figure 5 below presents the number of articles per journal.

4.4. Documents by Country or Territory

China and Indonesia are the countries that have conducted the most studies on the subject of the article, with five documents each. The United Kingdom and Palestine are next, with two documents each. Figure 6 displays the number of documents per top 10 country/territory. Figure 7 illustrates the density of the countries that contribute most to publications on this topic using VOS viewer software. It should be noted that the higher the density of countries, the more publications there will be.

4.5. Documents by Subject Area

Figure 8 provides a summary of the number of documents per area of work. It should be emphasized that only topics with more than one document were selected for this analysis. It should be emphasized that around 33% of the documents are in the field of environmental science and 22% are in computer science.

4.6. Keywords Analysis

The analysis of key words is important as it makes it possible to identify the main topics of each publication. Figure 9 presents the occurrence of key words using the WordArt software. Due to the substantial number of keywords, only those with more than two occurrences were analyzed. It should be mentioned that the larger the keywords, the greater the number of occurrences in the publications under analysis. Figure 9 shows that concepts such as COVID-19, Medical Waste and Hospital Waste are those with the most occurrences.
Figure 10 and Table 4 display the keyword co-occurrence network, with eight clusters identified (eight different colors). The words in orange belong to the seventh cluster, which includes the following words: nursing; simulation training and validation studies. Figure 11 gives a visualization of the density of the keywords.

5. Discussion

5.1. Generation Rate of Medical Waste

Hospitals, medical centers, laboratories, veterinary clinics, research centers, morgues, blood banks and nursing homes are the biggest sources of medical waste [63]. Only 15% of all medical waste is considered “hazardous waste”, which is infectious or toxic [12]. Developed countries are responsible for almost 11 kg of hazardous waste per hospital bed per day (kg/bed/day), while developing countries are responsible for 6 kg [64]. The volume of waste depends on various factors such as [65] the waste management technique, the type of healthcare institution, the amount of reusable equipment available and the number of patients treated each day.
Table 5 provides a list of some countries’ positions in the health system performance ranking and Figure 12 gives an analysis of the total production of medical waste in these countries.
A detailed analysis of Table 5 demonstrates that France and Spain have the best ranking in relation to the other countries under analysis, being in the top 10, while countries such as Brazil and Tanzania have the worst positions in this analysis (they are not in the top 100).
Figure 12 also shows that the country with the highest production of medical waste is the United States, with 10.7 kg/bed-day, while the lowest value is found in Tanzania, with only 0.14 kg/bed-day.
After an analysis, it can be seen that there is no relationship between the position in the health performance ranking and the total amount of medical waste generated by each country. The position of the United States (37th), with the highest amount of medical waste at 10.7 kg/bed-day generated, compared to the position of Tanzania (156th), with only 0.14, is worth noting.

5.2. Impact of COVID-19 Pandemic on Medical Waste Management and Environment

During the COVID-19 pandemic, the use of disposable safety equipment such as gloves, surgical masks and air purifiers rose abruptly and unexpectedly in order to stop the virus, therefore increasing the amount of hazardous medical waste [66]. The COVID-19 pandemic has led to an increase in medical waste around the world and a reduction in the capacity of healthcare professionals to manage it [67]. Good waste management through proper handling and use helps to reduce the negative effects on public health and the environment [5]. The COVID-19 pandemic has led to a significant increase in the generation and composition of municipal solid waste, reaching 2.5 million tons per month [28].
Sustainable medical waste management is a challenging issue, especially in emergency situations such as the COVID-19 pandemic [68]. Despite its adverse socio-economic impact, COVID-19 made it possible to reduce the level of environmental pollution by up to 30%, since, for example, air travel was reduced by 96%, and the emissions from factories were reduced [6].

5.3. Implications of Medical Waste for the Environment and Public Health

Pollution is seen as the biggest environmental problem, causing illness and premature death all over the world, with the health sector being a main contributor to acid rain, greenhouse gas emissions and the destruction of the atmosphere’s ozone layer [1]. It is estimated that hospitals and other healthcare facilities are some of the main contributors to environmental pollution, accounting for 4.4% of greenhouse gas emissions [69].
The incorrect management of hospital waste can lead to patients, healthcare professionals and waste managers being exposed to injuries, infections, toxic effects and air pollution [68]. Medical waste management is a task that puts human health and life at risk, especially in developing countries [64]. The poor management of waste produced by healthcare institutions can cause health concerns and create a negative impact on the ecosystem, affecting plants and animals [70].
The disposal of medical waste in uncontrolled areas leads to soil and groundwater contamination, directly affecting the environment [71]. The World Health Organization (WHO) stated in 2018 that, due to the use of infected syringes, there has been a spread of infectious diseases, with 21 million people developing hepatitis B, 2 million developing hepatitis C and 0.26 million developing HIV [69].

5.4. Medical Waste Management Process

The medical waste management process aims to minimize risks and control epidemic infections, and includes the processes of (i) collection, (ii) separation, (iii) storage, (iv) transportation, (v) processing and, finally, (vi) disposal [5]. Medical waste management is a series of steps starting from the generation of medical waste to safe disposal [72].
At the collection stage, medical waste is removed from healthcare institutions [73]. The main objective of transportation planning for medical waste is to produce as little pollution as possible from the collection site to the disposal centers [74].
The collection and transportation time, the length of the road, the transportation capacity and vehicle planning, and the type of disposal all play an essential role in more effective medical waste management [75]. The waste separation stage is a key step in the management process, since a mixture of infectious and non-infectious waste can lead to a total mixture of infectious waste, thus increasing disposal costs and exposing healthcare professionals to higher risks [76].
An increase in hospital waste implies major challenges for its treatment, for which there are various methods, such as microwaving, autoclaving and thermal treatment and disposal [77].

5.5. Research Questions

This systematic literature review is designed to answer two research questions. In this section, the research questions will be analyzed.
RQ1. What are the main difficulties in hospital waste supply chains?
The increased demand for more medical care has led to the greater production of medical waste. Much of it has to be treated differently from the other types due to its ability to transmit diseases. Hospital waste supply chains therefore need to respond effectively and efficiently to the large quantities of medical waste created by hospitals, health centers and research laboratories.
The proper transportation and treatment of medical waste is also a difficulty for supply chains due to its ability to transmit diseases and its negative impact on the environment.
RQ2. What contribution does simulation/hybrid simulation make to hospital waste supply chains?
Simulation is a tool that allows you to analyze different scenarios to see which is best for each decision-making process. It improves the performance of both medical waste transportation and treatment processes. One of the fundamental contributions of simulation is that it makes it possible to assess the behavior of a given system without directly affecting it, since these tests are carried out on computer models. With the constant increase in the complexity of systems, combining two or more simulation methods (hybrid simulation) allows for more precise and effective solutions than would be possible using just one simulation method.

6. Conclusions and Limitations

This work consisted of a systematic literature review of each concept central to the study: industry 4.0, simulation and the hospital waste supply chain. After this, discussion topics linked to the main theme were added.
The importance of the health sector has increased as the world’s population has grown. Everyone needs healthcare, which means that medical waste is being generated more and more. Since this waste can be infectious, greater care must be taken in its treatment and disposal.
Therefore, improving the performance of a hospital waste supply chain is the main focus of every manager in order to increase or maintain competitiveness and respond to people’s needs. The poor management of this waste can lead to harmful impacts on the environment, but also on living beings.
One of the tools that many supply chains have been using is simulation, one of the pillars of Industry 4.0, which allows complex systems to be modelled in order to study them and analyze the best scenario for more appropriate decision-making, while minimizing costs.
Simulation makes it possible to analyze various scenarios in a supply chain, allowing for better decision-making. The management of medical waste has stages, from its collection in healthcare institutions to its disposal.
In order to improve the performance of a given medical waste supply chain, different scenarios must be carried out to determine which is best, and simulation can make the medical waste supply chain more efficient.
The results indicate that the years 2020 and 2023 had the highest number of publications (with five each) and that countries such as China and Indonesia had the highest number of documents on this topic (with five each). The topics in this research area with more related documents were environmental sciences, computer sciences and medicine.
The study concluded that the amount of medical waste generated does not depend on the ranking of the health system’s performance, but rather on the difference between developed and developing countries.
In this systematic literature search, the PRISMA 2020 guidelines were used to select the appropriate publications for the topic. However, there is always the possibility that during this selection process, certain documents that are also important to the topic are not selected. Another limitation was the existence of various terminologies for one concept, which may have led to a search with fewer results than that using a particular terminology.
In conclusion, this study shows that simulation methods can be used to increase the performance of medical waste supply chains. In order to explore the subject further and reduce the existing gaps in simulation/hybrid simulation as a decision support tool for improving the performance of hospital waste supply chains, the following should be addressed in future research:
(i)
What the economic impact is in situations of good medical waste management;
(ii)
The existence of more practical cases in which simulation has been used as a tool for improving the performance of supply chains and the economic results and comparison of the situation before and after using simulation;
(iii)
The environmental impacts caused by medical waste and what hybrid simulation can do to improve sustainability.

Author Contributions

Author Contributions: Conceptualization, A.F. and A.L.R.; methodology, A.F., A.L.R., J.V.F. and L.P.F.; software, A.F.; validation, A.F., A.L.R., J.V.F. and L.P.F.; formal analysis, A.F.; investigation, A.F.; data curation, A.F.; writing—original draft preparation, A.F.; writing—review and editing, A.F., A.L.R., J.V.F. and L.P.F.; visualization, A.F.; supervision, A.L.R., J.V.F. and L.P.F.; project administration, A.L.R., J.V.F. and L.P.F.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kenny, C.; Priyadarshini, A. Review of Current Healthcare Waste Management Methods and Their Effect on Global Health. Healthcare 2021, 9, 284. [Google Scholar] [CrossRef] [PubMed]
  2. Ranjbari, M.; Shams Esfandabadi, Z.; Shevchenko, T.; Chassagnon-Haned, N.; Peng, W.; Tabatabaei, M.; Aghbashlo, M. Mapping Healthcare Waste Management Research: Past Evolution, Current Challenges, and Future Perspectives towards a Circular Economy Transition. J. Hazard. Mater. 2022, 422, 126724. [Google Scholar] [CrossRef] [PubMed]
  3. Dixit, A.; Routroy, S.; Dubey, S.K. A Systematic Literature Review of Healthcare Supply Chain and Implications of Future Research. Int. J. Pharm. Healthc. Mark. 2019, 13, 405–435. [Google Scholar] [CrossRef]
  4. Chauhan, A.; Singh, S.P. Selection of Healthcare Waste Disposal Firms Using a Multi-Method Approach. J. Environ. Manag. 2021, 295, 113117. [Google Scholar] [CrossRef] [PubMed]
  5. Erdem, M. Designing a Sustainable Logistics Network for Hazardous Medical Waste Collection a Case Study in COVID-19 Pandemic. J. Clean. Prod. 2022, 376, 134192. [Google Scholar] [CrossRef] [PubMed]
  6. Andeobu, L.; Wibowo, S.; Grandhi, S. Medical Waste from COVID-19 Pandemic—A Systematic Review of Management and Environmental Impacts in Australia. Int. J. Environ. Res. Public Health 2022, 19, 1381. [Google Scholar] [CrossRef] [PubMed]
  7. Bolan, S.; Padhye, L.P.; Kumar, M.; Antoniadis, V.; Sridharan, S.; Tang, Y.; Singh, N.; Hewawasam, C.; Vithanage, M.; Singh, L.; et al. Review on Distribution, Fate, and Management of Potentially Toxic Elements in Incinerated Medical Wastes. Environ. Pollut. 2023, 321, 121080. [Google Scholar] [CrossRef] [PubMed]
  8. Das, A.K.; Islam, M.N.; Billah, M.M.; Sarker, A. COVID-19 Pandemic and Healthcare Solid Waste Management Strategy—A Mini-Review. Sci. Total Environ. 2021, 778, 146220. [Google Scholar] [CrossRef]
  9. Goodarzian, F.; Ghasemi, P.; Gunasekaran, A.; Labib, A. A fuzzy sustainable model for COVID-19 medical waste supply chain network. Fuzzy Optim. Decis. Mak. 2024, 23, 93–127. [Google Scholar] [CrossRef]
  10. Xin, Y. Comparison of Hospital Medical Waste Generation Rate Based on Diagnosis-Related Groups. J. Clean. Prod. 2015, 100, 202–207. [Google Scholar] [CrossRef]
  11. Singh, N.; Ogunseitan, O.A.; Tang, Y. Medical Waste: Current Challenges and Future Opportunities for Sustainable Management. Crit. Rev. Environ. Sci. Technol. 2022, 52, 2000–2022. [Google Scholar] [CrossRef]
  12. Lee, S.M.; Lee, D.H. Effective Medical Waste Management for Sustainable Green Healthcare. Int. J. Environ. Res. Public Health 2022, 19, 14820. [Google Scholar] [CrossRef] [PubMed]
  13. Zonta, T.; da Costa, C.A.; da Rosa Righi, R.; de Lima, M.J.; da Trindade, E.S.; Li, G.P. Predictive Maintenance in the Industry 4.0: A Systematic Literature Review. Comput. Ind. Eng. 2020, 150, 106889. [Google Scholar] [CrossRef]
  14. Oláh, J.; Aburumman, N.; Popp, J.; Khan, M.A.; Haddad, H.; Kitukutha, N. Impact of Industry 4.0 on Environmental Sustainability. Sustainability 2020, 12, 4674. [Google Scholar] [CrossRef]
  15. Miśkiewicz, R.; Wolniak, R. Practical Application of the Industry 4.0 Concept in a Steel Company. Sustainability 2020, 12, 5776. [Google Scholar] [CrossRef]
  16. Vaidya, S.; Ambad, P.; Bhosle, S. Industry 4.0—A Glimpse. Procedia Manuf. 2018, 20, 233–238. [Google Scholar] [CrossRef]
  17. Müller, J.M.; Kiel, D.; Voigt, K.I. What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability. Sustainability 2018, 10, 247. [Google Scholar] [CrossRef]
  18. Sima, V.; Gheorghe, I.G.; Subić, J.; Nancu, D. Influences of the Industry 4.0 Revolution on the Human Capital Development and Consumer Behavior: A Systematic Review. Sustainability 2020, 12, 4035. [Google Scholar] [CrossRef]
  19. Ruiz-Sarmiento, J.R.; Monroy, J.; Moreno, F.A.; Galindo, C.; Bonelo, J.M.; Gonzalez-Jimenez, J. A Predictive Model for the Maintenance of Industrial Machinery in the Context of Industry 4.0. Eng. Appl. Artif. Intell. 2020, 87, 103289. [Google Scholar] [CrossRef]
  20. Windfeld, E.S.; Brooks, M.S.L. Medical Waste Management—A Review. J. Environ. Manag. 2015, 163, 98–108. [Google Scholar] [CrossRef]
  21. Prem Ananth, A.; Prashanthini, V.; Visvanathan, C. Healthcare Waste Management in Asia. Waste Manag. 2010, 30, 154–161. [Google Scholar] [CrossRef]
  22. Ouyang, L.; Zhu, Y.; Zheng, W.; Yan, L. An Information Fusion FMEA Method to Assess the Risk of Healthcare Waste. J. Manag. Sci. Eng. 2021, 6, 111–124. [Google Scholar] [CrossRef]
  23. Liu, S.; Zhang, J.; Niu, B.; Liu, L.; He, X. A Novel Hybrid Multi-Criteria Group Decision-Making Approach with Intuitionistic Fuzzy Sets to Design Reverse Supply Chains for COVID-19 Medical Waste Recycling Channels. Comput. Ind. Eng. 2022, 169, 108228. [Google Scholar] [CrossRef] [PubMed]
  24. Ghali, H.; Cheikh, A.B.; Bhiri, S.; Bouzgarrou, L.; Rejeb, M.B.; Gargouri, I.; Latiri, H.S. Health and Environmental Impact of Hospital Wastes: Systematic Review. Dubai Med. J. 2023, 6, 67–80. [Google Scholar] [CrossRef]
  25. Clauson, K.A.; Breeden, E.A.; Davidson, C.; Mackey, T.K. Leveraging Blockchain Technology to Enhance Supply Chain Management in Healthcare: An Exploration of Challenges and Opportunities in the Health Supply Chain. Blockchain Healthc. Today 2018, 1, 1–12. [Google Scholar] [CrossRef]
  26. Torkashvand, J.; Pasalari, H.; Jonidi-Jafari, A.; Kermani, M.; Nasri, O.; Farzadkia, M. Medical Waste Management in Iran and Comparison with Neighbouring Countries. Int. J. Environ. Anal. Chem. 2022, 102, 2805–2818. [Google Scholar] [CrossRef]
  27. Hsu, P.F.; Wu, C.R.; Li, Y.T. Selection of Infectious Medical Waste Disposal Firms by Using the Analytic Hierarchy Process and Sensitivity Analysis. Waste Manag. 2008, 28, 1386–1394. [Google Scholar] [CrossRef]
  28. Olabi, A.G.; Shehata, N.; Obaideen, K.; Sayed, E.T.; Mahmoud, M.; AlMallahi, M.N.; Abdelkareem, M.A. COVID-19: Medical Waste Management, Impact on Sustainable Development Goals, and Bibliometric Analysis. Chem. Eng. Technol. 2024, 47, 4–19. [Google Scholar] [CrossRef]
  29. Le, H.T.; Quoc, K.L.; Nguyen, T.A.; Dang, K.T.; Vo, H.K.; Luong, H.H.; Le Van, H.; Gia, K.H.; Cao Phu, L.V.; Nguyen Truong Quoc, D.; et al. Medical-Waste Chain: A Medical Waste Collection, Classification and Treatment Management by Blockchain Technology. Computers 2022, 11, 113. [Google Scholar] [CrossRef]
  30. Erboz, G. How to Define Industry 4.0: Main Pillars Of Industry 4.0. Manag. Trends Dev. Enterp. Glob. Era 2017, 761, 761–767. [Google Scholar]
  31. Xu, J.; Huang, E.; Hsieh, L.; Lee, L.H.; Jia, Q.S.; Chen, C.H. Simulation Optimization in the Era of Industrial 4.0 and the Industrial Internet. J. Simul. 2016, 10, 310–320. [Google Scholar] [CrossRef]
  32. Palmer, G.I.; Tian, Y. Implementing Hybrid Simulations That Integrate DES+SD in Python. J. Simul. 2023, 17, 240–256. [Google Scholar] [CrossRef]
  33. Scheidegger, A.P.G.; Pereira, T.F.; de Oliveira, M.L.M.; Banerjee, A.; Montevechi, J.A.B. An Introductory Guide for Hybrid Simulation Modelers on the Primary Simulation Methods in Industrial Engineering Identified through a Systematic Review of the Literature. Comput. Ind. Eng. 2018, 124, 474–492. [Google Scholar] [CrossRef]
  34. Brailsford, S.C.; Eldabi, T.; Kunc, M.; Mustafee, N.; Osorio, A.F. Hybrid Simulation Modelling in Operational Research: A State-of-the-Art Review. Eur. J. Oper. Res. 2019, 278, 721–737. [Google Scholar] [CrossRef]
  35. Ahsan, M.M.; Siddique, Z. Industry 4.0 in Healthcare: A Systematic Review. Int. J. Inf. Manag. Data Insights 2022, 2, 100079. [Google Scholar] [CrossRef]
  36. Fanta, G.B.; Pretorius, L.; Nunes, B. Enabling Circular Economy in Healthcare Using Industry 4.0 Digital Technologies. In Proceedings of the 30th International Conference of the International Association for Management of Technology, IAMOT 2021—MOT for the World of the Future, Cairo, Egypt, 19–23 September 2021; pp. 1111–1123. [Google Scholar] [CrossRef]
  37. Jayaraman, P.P.; Forkan, A.R.M.; Morshed, A.; Haghighi, P.D.; Kang, Y. Bin Healthcare 4.0: A Review of Frontiers in Digital Health. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2020, 10, 1–23. [Google Scholar] [CrossRef]
  38. Paul, S.; Riffat, M.; Yasir, A.; Mahim, M.N.; Sharnali, B.Y.; Naheen, I.T.; Rahman, A.; Kulkarni, A. Industry 4.0 Applications for Medical/Healthcare Services. J. Sens. Actuator Netw. 2021, 10, 43. [Google Scholar] [CrossRef]
  39. Shah, R.; Chircu, A. Iot and Ai in Healthcare: A Systematic Literature Review. Issues Inf. Syst. 2018, 19, 33–41. [Google Scholar] [CrossRef]
  40. Mengist, W.; Soromessa, T.; Legese, G. Method for Conducting Systematic Literature Review and Meta-Analysis for Environmental Science Research. MethodsX 2020, 7, 100777. [Google Scholar] [CrossRef]
  41. Donthu, N.; Kumar, S.; Mukherjee, D.; Pandey, N.; Lim, W.M. How to Conduct a Bibliometric Analysis: An Overview and Guidelines. J. Bus. Res. 2021, 133, 285–296. [Google Scholar] [CrossRef]
  42. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
  43. Sungkono, K.R.; Sarno, R.; Onggo, B.S.; Septiyanto, A.F. Optimising Waste Man-Agement Collaboration Processes Using Hybrid Modelling. Int. J. Simul. Model. 2024, 23, 53–64. [Google Scholar] [CrossRef]
  44. Liu, H.; Yao, Z.; Meijer, S. Research on Transportation Management Model of COVID-19 Medical Waste: A Case Study in Beijing, China. Environ. Sci. Pollut. Res. Int. 2023, 30, 120284–120299. [Google Scholar] [CrossRef] [PubMed]
  45. Arifin, M.; Hidaytulloh, S.; Sari, S.K. Discrete Cuckoo Search Algorithm in Scheduling Dynamic Route of Medical and Non-Medical Waste Transportation at Regional-Based Health Facilities during the COVID-19 Pandemic. AIP Conf. Proc. 2023, 2693, 030022. [Google Scholar] [CrossRef]
  46. Nosrati-Abarghooee, S.; Sheikhalishahi, M.; Nasiri, M.M.; Gholami-Zanjani, S.M. Designing Reverse Logistics Network for Healthcare Waste Management Considering Epidemic Disruptions under Uncertainty. Appl. Soft Comput. 2023, 142, 110372. [Google Scholar] [CrossRef]
  47. Shaju, S.; George, T.; Francis, J.K.; Joseph, M.; Thomas, M.J. Conceptual Design and Simulation Study of an Autonomous Indoor Medical Waste Collection Robot. IAES Int. J. Robot. Autom. 2023, 12, 29–40. [Google Scholar] [CrossRef]
  48. Maziya, F.B.; Luthi, R.S.; Iresha, F.M.; Abidin, A.U. The Influence of Pandemic COVID-19 on Hazardous Waste Management from Hospital A in Yogyakarta. Proc. IOP Conf. Ser. Earth Environ. Sci. 2023, 1257, 012014. [Google Scholar]
  49. Xiao, H.; Ma, C.; Gao, H.; Gao, Y.; Xue, Y. Green Transformation of Anti-Epidemic Supplies in the Post-Pandemic Era: An Evolutionary Approach. Int. J. Environ. Res. Public Health 2022, 19, 6011. [Google Scholar] [CrossRef]
  50. Sungkono, K.R.; Dewa Prakasa, T.A.; Hermawan, A.R.; Bhamakerti, G.A.; Sarno, R. Optimizing Time and Cost Activity Based on Discrete Event Simulation with Multi-Objective Optimization Method by Ratio Analysis (MOORA). In Proceedings of the Proceedings—ICACSIS 2022: 14th International Conference on Advanced Computer Science and Information Systems; Institute of Electrical and Electronics Engineers Inc.: New York, NY, USA, 2022; pp. 53–58. [Google Scholar]
  51. Liu, Z.; Liu, T.; Liu, X.; Wei, A.; Wang, X.; Yin, Y.; Li, Y. Research on Optimization of Healthcare Waste Management System Based on Green Governance Principle in the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2021, 18, 5316. [Google Scholar] [CrossRef]
  52. Aydın, N. A Comprehensive Waste Management Simulation Model for the Assessment of Waste Segregation in the Health Sector. Environ. Eng. Manag. J. 2021, 20, 1731–1738. [Google Scholar] [CrossRef]
  53. Peyman, M.; Li, Y.; Tordecilla, R.D.; Copado-Mendez, P.J.; Juan, A.A.; Xhafa, F. Waste Collection of Medical Items under Uncertainty Using Internet of Things and City Open Data Repositories: A Simheuristic Approach. In Proceedings of the Winter Simulation Conference; Institute of Electrical and Electronics Engineers Inc.: New York, NY, USA, 2021. [Google Scholar]
  54. Mulyadi, A.; Arvitrida, N.I. Managing Medical Waste during COVID-19 Outbreak: A Simulation Approach. Proc. Int. Conf. Ind. Eng. Oper. Manag. 2021, 379–390. [Google Scholar] [CrossRef]
  55. Irfa’i, M.; Arifin, A.; Kriswandana, F.; Thohari, I. The Design of Medical Waste Treat-Ment in Public Health Center (MWT-P) for Reducing Total Bacteria Count in Banjarbaru. J. Kesehat. Lingkung. 2020, 12, 254–261. [Google Scholar] [CrossRef]
  56. Liu, W.; Guo, J.; Yao, F.; Chen, D. Adaptive Protocol Generation for Group Collaborative in Smart Medical Waste Transportation. Futur. Gener. Comput. Syst. 2020, 110, 167–180. [Google Scholar] [CrossRef]
  57. Liu, Z.; Li, Z.; Chen, W.; Zhao, Y.; Yue, H.; Wu, Z. Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune–Ant Colony Algorithm. Int. J. Environ. Res. Public Health 2020, 17, 5831. [Google Scholar] [CrossRef] [PubMed]
  58. Abdelall, S.; Baroud, D.; Alalamy, S.; Alrass, I.; Agha, S. The Use of Discrete Event Simulation for Optimal Performance of Blood Banks (A Case Study of Al-Shifa Central Blood Bank). In Proceedings of the 2020 International Conference on Assistive and Rehabilitation Technologies, iCareTech 2020; Institute of Electrical and Electronics Engineers Inc.: New York, NY, USA, 2020; pp. 36–40. [Google Scholar]
  59. Daoud, R.; Kammoun, M.; Hachicha, W. Solving a Routing Problem of Collect Infectious Healthcare Waste with Stochastic Demand: Case of Sfax Governorate in Tunisia. World Rev. Intermodal Transp. Res. 2020, 9, 297–311. [Google Scholar] [CrossRef]
  60. Mantzaras, G.; Voudrias, E.A. An Optimization Model for Collection, Haul, Transfer, Treatment and Disposal of Infectious Medical Waste: Application to a Greek Region. Waste Manag. 2017, 69, 518–534. [Google Scholar] [CrossRef] [PubMed]
  61. Al-Khatib, I.A.; Eleyan, D.; Garfield, J. A System Dynamics Approach for Hospital Waste Management in a City in a Developing Country: The Case of Nablus, Palestine. Environ. Monit. Assess. 2016, 188, 503. [Google Scholar] [CrossRef] [PubMed]
  62. Eduardo, A.H.A.; Mendes, A.A.; Binotto, C.C.S.; Tognoli, S.H.; Tucci, A.M.G.B. Scenario for a Simulation of Health Services’ Waste: A Methodological Study. Online Braz. J. Nurs. 2016, 15, 611–616. [Google Scholar] [CrossRef]
  63. Taslimi, M.; Batta, R.; Kwon, C. Medical Waste Collection Considering Transportation and Storage Risk. Comput. Oper. Res. 2020, 120, 104966. [Google Scholar] [CrossRef]
  64. Janik-Karpinska, E.; Brancaleoni, R.; Niemcewicz, M.; Wojtas, W.; Foco, M.; Podogrocki, M.; Bijak, M. Healthcare Waste—A Serious Problem for Global Health. Healthcare 2023, 11, 242. [Google Scholar] [CrossRef] [PubMed]
  65. Bokhoree, C.; Beeharry, Y.; Makoondlall-Chadee, T.; Doobah, T.; Soomary, N. Assessment of Environmental and Health Risks Associated with the Management of Medical Waste in Mauritius. APCBEE Procedia 2014, 9, 36–41. [Google Scholar] [CrossRef]
  66. Tushar, S.R.; Alam MF, B.; Bari, A.M.; Karmaker, C.L. Assessing the Challenges to Medical Waste Management during the COVID-19 Pandemic: Implications for the Environmental Sustainability in the Emerging Economies. Socioecon. Plan. Sci. 2023, 87, 101513. [Google Scholar] [CrossRef] [PubMed]
  67. Kuntari, N.A.N.; Ulfa, M.; Jamal, A. The Impact of COVID-19 Pandemic on Hospital Waste Management: A Bibliometric Analysis. Avicenna J. Environ. Health Eng. 2023, 10, 110–119. [Google Scholar] [CrossRef]
  68. Sarkodie, S.A.; Owusu, P.A. Impact of COVID-19 Pandemic on Waste Management. Environ. Dev. Sustain. 2021, 23, 7951–7960. [Google Scholar] [CrossRef]
  69. Chew, X.Y.; Khaw, K.W.; Alnoor, A.; Ferasso, M.; Al Halbusi, H.; Muhsen, Y.R. Circular Economy of Medical Waste: Novel Intelligent Medical Waste Management Framework Based on Extension Linear Diophantine Fuzzy FDOSM and Neural Network Approach. Environ. Sci. Pollut. Res. 2023, 30, 60473–60499. [Google Scholar] [CrossRef] [PubMed]
  70. Nosheen, F.; Malik, N.; Mehmood, Z.; Jabeen, F.; Mahmood, A.; Ibrahim, M.; Bokhari, A.; Mubashir, M.; Chuah, L.F.; Show, P.L. Biomedical Waste Management Associated with Infectious Diseases among Health Care Professionals in Apex Hospitals of a Typical South Asian City. Environ. Res. 2022, 215, 114240. [Google Scholar] [CrossRef] [PubMed]
  71. Meleko, A.; Tesfaye, T.; Henok, A. Assessment of Healthcare Waste Generation Rate and Its Management System in Health Centers of Bench Maji Zone. Ethiop. J. Health Sci. 2018, 28, 125–134. [Google Scholar] [CrossRef] [PubMed]
  72. Attrah, M.; Elmanadely, A.; Akter, D.; Rene, E.R. A Review on Medical Waste Management: Treatment, Recycling, and Disposal Options. Environments 2022, 9, 146. [Google Scholar] [CrossRef]
  73. Gao, J.; Li, H.; Wu, J.; Lyu, J.; Tan, Z.; Jin, Z. Routing Optimisation of Urban Medical Waste Recycling Network Considering Differentiated Collection Strategy and Time Windows. Sci. Program. 2021, 2021, 5523910. [Google Scholar] [CrossRef]
  74. Babaee Tirkolaee, E.; Aydın, N.S. A Sustainable Medical Waste Collection and Transportation Model for Pandemics. Waste Manag. Res. 2021, 39, 34–44. [Google Scholar] [CrossRef]
  75. Çelik, S.; Peker, İ.; Gök-Kısa, A.C.; Büyüközkan, G. Multi-Criteria Evaluation of Medical Waste Management Process under Intuitionistic Fuzzy Environment: A Case Study on Hospitals in Turkey. Socioecon. Plan. Sci. 2023, 86, 101499. [Google Scholar] [CrossRef]
  76. Amariglio, A.; Depaoli, D. Waste Management in an Italian Hospital’s Operating Theatres: An Observational Study. Am. J. Infect. Control 2021, 49, 184–187. [Google Scholar] [CrossRef]
  77. Cai, X.; Du, C. Thermal Plasma Treatment of Medical Waste. Plasma Chem. Plasma Process. 2021, 41, 1–46. [Google Scholar] [CrossRef]
Sustainability 16 06187 g001
Figure 1. Research methodology.
Figure 1. Research methodology.
Sustainability 16 06187 g001
Sustainability 16 06187 g002
Figure 2. Prisma 2020 flowchart.
Figure 2. Prisma 2020 flowchart.
Sustainability 16 06187 g002
Sustainability 16 06187 g003
Figure 3. Documents by year.
Figure 3. Documents by year.
Sustainability 16 06187 g003
Sustainability 16 06187 g004
Figure 4. Documents by type.
Figure 4. Documents by type.
Sustainability 16 06187 g004
Sustainability 16 06187 g005
Figure 5. Number of articles by source.
Figure 5. Number of articles by source.
Sustainability 16 06187 g005
Sustainability 16 06187 g006
Figure 6. Documents by country or territory.
Figure 6. Documents by country or territory.
Sustainability 16 06187 g006
Sustainability 16 06187 g007
Figure 7. Density visualization (country).
Figure 7. Density visualization (country).
Sustainability 16 06187 g007
Sustainability 16 06187 g008
Figure 8. Documents by subject area.
Figure 8. Documents by subject area.
Sustainability 16 06187 g008
Sustainability 16 06187 g009
Figure 9. Keyword occurrence.
Figure 9. Keyword occurrence.
Sustainability 16 06187 g009
Sustainability 16 06187 g010
Figure 10. Keywords Co-occurrence network visualization.
Figure 10. Keywords Co-occurrence network visualization.
Sustainability 16 06187 g010
Sustainability 16 06187 g011
Figure 11. Keywords Density Visualization.
Figure 11. Keywords Density Visualization.
Sustainability 16 06187 g011
Sustainability 16 06187 g012
Figure 12. Total healthcare waste generation (kg/bed-day) (Adapted from [20]).
Figure 12. Total healthcare waste generation (kg/bed-day) (Adapted from [20]).
Sustainability 16 06187 g012
Table 1. Number of documents identified in the databases.
Table 1. Number of documents identified in the databases.
ScopusScienceDirect
9721
Table 2. Eligibility Criteria.
Table 2. Eligibility Criteria.
Inclusion CriteriaExclusion Criteria
All documents dated since 2010Non-English;
Vaguely related to the theme
Table 3. Main information about publications selected.
Table 3. Main information about publications selected.
TitleYearMain Objective, Methodology and ResultsNº of Citations (Scopus)
Optimizing Waste Management Collaboration Processes Using Hybrid Modelling [43]2024This work proposes hybrid modeling with integrated discrete-event simulation, agent-based simulation and improved MCDC methods in order to optimize the number of workers with the minimum asynchronous waiting time and cost based on waste management process data. The results indicate that hybrid modelling can minimize 74% of the minimum asynchronous waiting time and 31% of the activity cost compared to the actual model under an overload condition.0
Research on transportation management model of COVID-19 medical waste: a case study in Beijing, China [44]2023This article implements a transport route optimization model using Anylogic simulation software in the regional distribution of 118 tertiary hospitals and two large medical waste disposal plants in Beijing. This study enabled the analysis of two modes of hospital waste transportation (the most costly route and the fastest speed), contributing to the better management of hospital waste transportation.0
Discrete cuckoo search algorithm in scheduling dynamic route of medical and non-medical waste transportation at regional-based health facilities during the COVID-19 pandemic [45]2023This work designs waste transport routes in such a way as to minimize the costs and distance using the Discrete Cuckoo Search algorithm. In this paper, a clustering process was carried out and then the algorithm was implemented. Finally, a comparison was made with other algorithms. The results reveal that the Discrete Cuckoo search algorithm had better results than the Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). 0
Designing reverse logistics network for healthcare waste management considering epidemic disruptions under uncertainty [46]2023The main aim of this study was to create a reverse logistics network to manage healthcare waste. The model minimizes the total cost and the risk to the population. A simulation algorithm using probabilistic distribution functions was implemented in order to generate data of different sizes and to evaluate the proposed model.3
Conceptual design and simulation study of an autonomous indoor medical waste collection robot [47]2023The purpose of this article was to describe a project for a mobile robot whose tasks consist of intelligently identifying and collecting a group of hospital wastes. To achieve this, several simulations were created using the Robot Operating System and the Gazebo simulator. A LIDAR sensor was also implemented to monitor the robot’s surroundings and enable autonomous navigation.1
The Influence of Pandemic COVID-19 on Hazardous Waste Management from Hospital A in Yogyakarta [48]2023The main objective of this study was to analyze the management of hazardous waste during the pandemic in a hospital in Yogyakarta. For this purpose, dynamic systems modeling was used for the technical, financial and environmental aspects using Vensim PLE with a stock–flow diagram.
Dynamic system modeling applications were used, considering technical, financial and environmental aspects, using Vensim PLE with the stock–flow diagram.
The results showed that there was an increase in the production and transportation of waste during the pandemic, reaching 9.36%. In total, 11,756 kg/year of waste was produced and the waste transportation costs increased to IDR 109,860,000/year.		0
Green Transformation of Anti-Epidemic Supplies in the Post-Pandemic Era: An Evolutionary Approach [49]2022This work investigated a method of effectively guiding the green transformation of medical waste concerning the economic interests of medical institutions and manufacturers of masks. In this paper, a dynamic system model was implemented for the simulation analysis.4
Optimizing Time and Cost Activity based on Discrete Event Simulation with Multi-Objective Optimization Method by Ratio Analysis (MOORA) [50]2022The main purpose of this work was to determine the number of medical resources in different proportions of time and cost in an attempt to identify the relationship between cost, time and the number of workers involved in the waste management system of a hospital in East Java in Indonesia, through a combination of Discrete Event Simulation and MOORA. The results showed that less incoming waste requires fewer workers than in situations with more waste.0
Research on optimization of healthcare waste management system based on green governance principle in the COVID-19 pandemic [51]2021This article analyzes the principles of green governance and highlights the problems that exist in the healthcare waste management system in Wuhan. The work proposes a hybrid model combining the Genetic Algorithm (GA) and Ant Colony Algorithm (ACO) to achieve hospital waste transportation optimization. Finally, this article analyzes the role of government, hospitals and communities in the process of disposing of healthcare waste and suggests guidelines for its disposal.15
A comprehensive waste management simulation model for the assessment of waste segregation in the health sector [52]2021The main goal of this study was to evaluate different levels of segregation in household waste mixed with hospital waste in the Thrace Region of Turkey. Therefore, the Stella and Vensim Simulation was implemented to evaluate medical waste flows. The results indicated a predicted increase from almost 2000 tons/year to almost 3000 tons/year in 2045. There is also the possibility of avoiding 300 tons of hospital waste per year by reducing the domestic content of hospital waste to 50%.1
Waste Collection of Medical Items under Uncertainty Using Internet of Things and City Open Data Repositories: A Simheuristic Approach Open Access [53]2021This work explores the problem of collecting medical waste in the city of Barcelona, minimizing the total time invested by the fleet of vehicles to complete the task. Thus, the combination of multi-start biased–randomized heuristics (BRHs) with Monte Carlo Simulation allows more effective results in situations where the travel and collection time are uncertain. 0
Managing Medical Waste during COVID-19 Outbreak: A Simulation Approach [54]2021The aims of this study were to explore the strategies and policies needed to manage the amount of medical waste in Indonesia in two situations: during an outbreak such as COVID-19 and under normal conditions (no outbreak). Systems Dynamics is used in this study and helps to optimize the medical waste management system during COVID-19.0
The design of medical waste treatment in public health center (MWT-P) for reducing total bacteria count in Banjarbaru [55]2020This study explores variations (contact time and chlorine dose variation) in the different stages of hospital waste processing in the Public Health Center of the city of Banjarbaru. This study was conducted by simulating variations. The study results indicated that the use of MWT-P decreases the number of microorganisms or bacteria in medical waste. 2
Adaptive protocol generation for group collaborative in smart medical waste transportation [56]2020This work deals with the challenge of collective decision-making between intelligent components in the transportation of medical waste by using Automated Guided Vehicle Medical Waste Transportation (AGV-MWT). 4
Path optimization of medical waste transport routes in the emergent public health event of COVID-19: A hybrid optimization algorithm based on the immune-ant colony algorithm [57]2020This article applies an immunological algorithm to establish a model for locating urban medical waste storage sites in Wuhan, China. In this work, various temporary storage points are analyzed according to the environmental impacts and evaluation criteria, using the Q-value method to allocate hospital waste vehicles and applying the immune ant colony algorithm along with the tabu search algorithm. 28
The use of discrete event simulation for optimal performance of blood banks (A case study of Al-Shifa Central Blood Bank) [58]2020The aim of this study was to satisfy the need for blood while minimizing the phenomenon of outdated blood units. Discrete Event Simulation was implemented to represent the Al-Shifa Central Blood Bank in order to enable better decision-making by analyzing the system’s behavior.0
Solving a routing problem of collect infectious healthcare waste with stochastic demand: Case of Sfax governorate in Tunisia [59]2020This work analyzes approaches that help municipalities make decisions on how to implement an appropriate system for transporting infectious healthcare waste. Two approaches were proposed: (1) a combination of the exact method with Monte Carlo simulation, and (2) a combination of the same simulation tools with those proposed by Clarke and Wright (C&W); this made it possible to support decision-making in the creation of a transport system for infectious medical waste. 0
An optimization model for collection, haul, transfer, treatment and disposal of infectious medical waste: Application to a Greek region [60]2017This paper presents an optimization model that minimizes the cost of a system for collecting, transporting, transferring, treating and disposing of infectious hospital waste (optimal location of treatment facilities and transfer stations, their design capacities (t/d), the number and capacity of all waste collection sites, and transportation and transfer vehicles and their optimal transportation route).
The model was implemented in the East Macedonia–Thrace region of Greece
Two software packages were used: Evolver, which is based on the use of genetic algorithms, and Crystal Ball, which is based on Monte Carlo simulation.		74
A system dynamics approach for hospital waste management in a city in a developing country: the case of Nablus, Palestine [61]2016This study focuses on the development of a system dynamics simulation model for use as a predictive tool in hospital waste management for better decision-making. Through this model, it is possible to analyze different future scenarios of hospital waste situations in Palestine. The model developed allows for a comparison of the total amount of waste produced between different hospitals and to predict the generation of more waste, as well as treatment costs.22
Scenario for a simulation of health services’ waste: A methodological study [62]2016This methodological study aims to validate the different contents of a scenario to be used in the form of a healthcare waste management simulation. Based on the scenario implemented and improved by experts and students, it will be possible to train healthcare professionals in different contexts.4
Table 4. Cluster from keywords.
Table 4. Cluster from keywords.
Cluster 1Cluster 2Cluster 3Cluster 4
COVID-19;
Environmentally friendly masks;
Evolutionary game;
Government regulation;
Green Transformation;
Simulation;
Waste generation		Developing countries;
Generation rate;
Hospitals;
Palestine;
System Dynamics		Domestic waste;
Hazardous waste;
Medical waste flows;
Simulation modeling;
System parameters		Ant colony algorithm;
Immune tabu search algorithm;
Path Optimization;
Transit storage
Cluster 5Cluster 6Cluster 7Cluster 8
Fastest speed;
Shortest path;
Tertiary hospital;
Transportation		Dynamic modelling;
Generation;
Pandemic		Nursing;
Simulation training;
Validation studies		Bacteria;
Medical waste;
Treatment plan
Table 5. WHO ranking of health system performance (Adapted from [20]).
Table 5. WHO ranking of health system performance (Adapted from [20]).
CountryWHO Ranking of Health System Performance
Norway11
United States37
United Kingdom18
France1
Spain7
Brazil125
Turkey70
Jordan83
Pakistan122
Tanzania156
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ferreira, A.; Ramos, A.L.; Ferreira, J.V.; Ferreira, L.P. Simulation of Hospital Waste Supply Chain in the Context of Industry 4.0—A Systematic Literature Review. Sustainability 2024, 16, 6187. https://doi.org/10.3390/su16146187

AMA Style

Ferreira A, Ramos AL, Ferreira JV, Ferreira LP. Simulation of Hospital Waste Supply Chain in the Context of Industry 4.0—A Systematic Literature Review. Sustainability. 2024; 16(14):6187. https://doi.org/10.3390/su16146187

Chicago/Turabian Style

Ferreira, André, Ana L. Ramos, José V. Ferreira, and Luís P. Ferreira. 2024. "Simulation of Hospital Waste Supply Chain in the Context of Industry 4.0—A Systematic Literature Review" Sustainability 16, no. 14: 6187. https://doi.org/10.3390/su16146187

APA Style

Ferreira, A., Ramos, A. L., Ferreira, J. V., & Ferreira, L. P. (2024). Simulation of Hospital Waste Supply Chain in the Context of Industry 4.0—A Systematic Literature Review. Sustainability, 16(14), 6187. https://doi.org/10.3390/su16146187

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop