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Voudrias EA. Management of COVID-19 healthcare waste based on the circular economy hierarchy: A critical review. WASTE MANAGEMENT & RESEARCH : THE JOURNAL OF THE INTERNATIONAL SOLID WASTES AND PUBLIC CLEANSING ASSOCIATION, ISWA 2024; 42:977-996. [PMID: 37753975 DOI: 10.1177/0734242x231198424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
The overall objective of this work was to conduct a critical literature review on the application of the circular economy (CE) hierarchy for the management of COVID-19 healthcare waste (HCW). To describe the problem created by COVID-19 HCW, first, the subsystems of the overall management system, including generation, segregation, classification, storage, collection, transport, treatment and disposal, were reviewed and briefly described. Then, the CE hierarchy using the 10R typology was adapted to the management of COVID-19 HCW and included the strategies Refuse, Reduce, Resell/Reuse, Repair, Reprocess, Refurbish, Remanufacture, Repurpose, Recycle and Recover (energy). Disposal was added as a sink of residues from the CE strategies. Using the detailed 10R CE hierarchy for COVID-19 HCW management is the novelty of this review. It was concluded that R-strategy selection depends on its position in the CE hierarchy and medical item criticality and value. Indicative HCW components, which can be managed by each R-strategy, were compiled, but creating value by recovering infectious downgraded materials contaminated with body fluids and tissues is not currently possible. Therefore, after applying the circular solutions, the end of pipe treatment and disposal would be necessary to close material cycles at the end of their life cycles. Addressing the risks, knowledge gaps and policy recommendations of this article may help to combat COVID-19 and future pandemics without creating environmental crises.
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Affiliation(s)
- Evangelos A Voudrias
- Department of Environmental Engineering, Democritus University of Thrace, Xanthi, Greece
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2
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Chen W, Liu Y, Han M. Designing a sustainable reverse logistics network for used cell phones based on offline and online trading systems. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 354:120417. [PMID: 38382439 DOI: 10.1016/j.jenvman.2024.120417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/26/2024] [Accepted: 02/15/2024] [Indexed: 02/23/2024]
Abstract
Unsustainable production and consumption are driving a significant increase in global electronic waste, posing substantial environmental and human health risks. Even in more developed nations, there is the challenge of low collection rates. In response, we integrate offline and online trading systems and design a material efficiency strategy for used cell phones. We propose a new multi-objective optimization framework to maximize profit, carbon emissions reduction, and circularity in the process of recycling and treatment. Considering multi-period, multi-product, multi-echelon features, as well as price sensitive demand, incentives, and qualities, we established a new multi-objective mixed-integer nonlinear programming optimization model. An enhanced, Fast, Non-Dominated Solution Sorting Genetic Algorithm (ASDNSGA-II) is developed for the solution. We used operational data from a leading Chinese Internet platform to validate the proposed optimization framework. The results demonstrate that the reverse logistics network designed achieves a win-win situation regarding profit and carbon emission reduction. This significantly boosts confidence and motivation for engaging in recycling efforts. Online recycling shows robust profitability and carbon reduction capabilities. An effective coordination mechanism for pricing in both online and offline channels should be established, retaining offline methods while gradually transitioning towards online methods. To increase the collection rate, it is essential to jointly implement a transitional strategy, including recycling incentives and subsidy policies. Additionally, elevating customer environmental awareness should be viewed as a long-term strategy, mitigating the cost of increasing collection rates during the market maturity stage (high collection rates).
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Affiliation(s)
- Weidong Chen
- College of Management and Economics, Tianjin University, TianJin, 300072, China.
| | - Yong Liu
- College of Management and Economics, Tianjin University, TianJin, 300072, China.
| | - Mingzhe Han
- College of Management and Economics, Tianjin University, TianJin, 300072, China.
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Liu H, Yao Z, Meijer S. Research on transportation management model of COVID-19 medical waste: a case study in Beijing, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:120284-120299. [PMID: 37936037 DOI: 10.1007/s11356-023-30605-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 10/18/2023] [Indexed: 11/09/2023]
Abstract
During the COVID-19 pandemic, disposable masks, protective clothing, gloves, and nasopharyngeal swabs collected by nucleic acid testing formed a large amount of medical waste. Medical waste has strict temporary storage time requirements in hospitals, which need to be transported to medical waste disposal plants within the specified time. However, as most of disposal plants are far away from downtown, they also need to be responsible for the transportation and disposal of medical waste in many hospitals, and put forward higher requirement for transportation routes. Rapid and safe disposal of all types of medical waste generated by COVID-19 is crucial to the prevention and control of the epidemic. This paper designs the transportation route optimization model using Anylogic simulation software based on the regional distribution of 118 tertiary hospitals and 2 large medical waste disposal plants in Beijing, China. At the same time, transportation routes of 118 tertiary hospitals in the morning peak, evening peak, all-day, and ordinary periods were simulated based on the Beijing traffic index in 2017. On this basis, through the analysis of the simulation data, the selection of medical waste transport routes for 118 tertiary hospitals in the morning peak, evening peak, all day, and ordinary periods is further clarified, so as to ensure that medical waste can be transported to the medical waste disposal plant in the shortest time. The shortest path and fastest speed transport mode, medical waste transport data set, and the selection of transport mode of 118 tertiary hospitals formed by this research provide certain reference experience for the rapid and safe transport of medical waste during the epidemic period, and also provides corresponding data support for medical waste transportation management in the post-epidemic era and medical waste transportation decision-making when facing major public health problems.
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Affiliation(s)
- Hao Liu
- Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China.
- School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 14157, Huddinge, Stockholm, Sweden.
| | - Zhong Yao
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Sebastiaan Meijer
- School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 14157, Huddinge, Stockholm, Sweden
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Darvazeh SS, Mooseloo FM, Aeini S, Vandchali HR, Tirkolaee EB. An integrated methodology for green human resource management in construction industry. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:124619-124637. [PMID: 35641740 PMCID: PMC9154213 DOI: 10.1007/s11356-022-20967-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/16/2022] [Indexed: 06/15/2023]
Abstract
Today, by increasing public awareness about environmental issues and pressures from governments and other stakeholders, companies have dealt with environmental challenges more than ever. This paper focuses on environmentally sustainable performance using an integrated methodology based on meta-synthesis, Delphi, and structural equation modeling (SEM) techniques which are utilized in different phases. In the first phase, an in-depth review of green human resources management (GHRM) literature is conducted based on the meta-synthesis method, and as a result, 38 codes are extracted. Next, to adapt and customize the codes with the nature of the construction industry, 2 rounds of Delphi method are implemented to extract the expert judgment from a panel of 15 industry professionals, resulting in 21 codes in 7 categories. To validate the developed methodology, a dataset from 33 Iranian construction companies are collected along with 15 factors in 5 categories determined using SEM. The findings reveal that among 9 main GHRM components extracted from the literature, just 5 components including green recruitment and selection, green performance management, green-reward, green-based employee empowerment, and green training have significant and positive relationships with GHRM. Finally, managerial insights, limitations, and future research directions are discussed.
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Affiliation(s)
- Saeid Sadeghi Darvazeh
- Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran
| | - Farzaneh Mansoori Mooseloo
- Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran
| | - Samira Aeini
- Department of Project and Construction Management, Noore-Touba University, Tehran, Iran
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Nosrati-Abarghooee S, Sheikhalishahi M, Nasiri MM, Gholami-Zanjani SM. Designing reverse logistics network for healthcare waste management considering epidemic disruptions under uncertainty. Appl Soft Comput 2023; 142:110372. [PMID: 37168874 PMCID: PMC10154062 DOI: 10.1016/j.asoc.2023.110372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/13/2023]
Abstract
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.
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Affiliation(s)
| | - Mohammad Sheikhalishahi
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mohammad Mahdi Nasiri
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
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Chew X, Khaw KW, Alnoor A, Ferasso M, Al Halbusi H, Muhsen YR. Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:60473-60499. [PMID: 37036648 PMCID: PMC10088637 DOI: 10.1007/s11356-023-26677-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/23/2023] [Indexed: 04/11/2023]
Abstract
Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.
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Affiliation(s)
- XinYing Chew
- School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Khai Wah Khaw
- School of Management, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Alhamzah Alnoor
- Management Technical College, Southern Technical University, Basrah, Iraq.
| | - Marcos Ferasso
- Economics and Business Sciences Department, Universidade Autónoma de Lisboa, 1169-023, Lisbon, Portugal
| | - Hussam Al Halbusi
- Department of Management, Ahmed Bin Mohammad Military College, Doha, Qatar
| | - Yousif Raad Muhsen
- Faculty of Engineering, Universiti Putra Malaysia, Seri Kembangan, Selangor, Malaysia
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7
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Taheri F, Moghaddam BF. A heuristic-based hybrid algorithm to configure a sustainable supply chain network for medical devices considering information-sharing systems. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:91105-91126. [PMID: 35882735 PMCID: PMC9321313 DOI: 10.1007/s11356-022-22147-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
In today's hyper-competitive marketplace, the crucial role of the sustainability concept has been highlighted more. Hence, managers' attention has been attracted to the concept of sustainable supply chains. On the other hand, after the COVID-19 outbreak, the importance of medical devices and their demand has drastically enhanced, which has led to shifting the attention of researchers toward this industry. In this regard, based on the importance of the mentioned points, the current study configures a sustainable supply chain network for the medical devices industry. In this way, given the crucial role of the oxygen concentrator during the COVID-19 outbreak, the present study investigates the supply chain of the mentioned goods as a case study. Also, this research develops an efficient hybrid solution method based on goal programming, a heuristic algorithm, and the simulated annealing algorithm to solve the suggested model. Eventually, sensitivity analysis is conducted to examine the influence of the crucial parameters of the model on the outputs, and managerial insights are provided. According to the achieved results, the suggested model and the developed hybrid method demonstrate a good performance which shows their efficiency.
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Affiliation(s)
- Farid Taheri
- Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran.
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Erdem M. Designing a sustainable logistics network for hazardous medical waste collection a case study in COVID-19 pandemic. JOURNAL OF CLEANER PRODUCTION 2022; 376:134192. [PMID: 36158600 PMCID: PMC9487203 DOI: 10.1016/j.jclepro.2022.134192] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/29/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
The process of collecting and transporting hazardous medical waste poses a potential threat to the environment and public safety. Furthermore, the waste management system faces higher transportation costs due to the increasing human activities related to rapid population growth. The absence of an efficient and safe logistics network for the timely collection and transportation of hazardous wastes may have negative effects on the environment and public health. Therefore, more sustainable transportation of hazardous waste services is a necessity This paper attempts to design a sustainable network for hazardous medical waste collection services during the COVID-19 pandemic. An electric medical waste collection vehicle routing problem is introduced to construct optimal routes and rosters for a fleet of electric vehicles as well as cover their choice of charging technologies, times and locations. This problem allows us to minimize the health risk of hazardous medical waste while providing cost-effective, zero-emission waste management logistics. Therefore, this problem covers environmental and economic objectives to achieve sustainable development. An effective heuristic that covers adaptive large neighbourhood search and a local search is designed to deal with the complex problem. A series of extensive computational experiments is carried out using real-life benchmark instances to assess the performance of the algorithm. A sensitivity analysis is also conducted to investigate the effect of multiple charger types on the cost and risk objectives. The experiment results indicate that mixed-use of different charger types can reduce the total energy cost and transport risk compared to the case of using only a single charger.
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Affiliation(s)
- Mehmet Erdem
- Department of Industrial Engineering, Ondokuz Mayıs University, Samsun, Turkey
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9
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Lotfi R, Kargar B, Gharehbaghi A, Weber GW. Viable medical waste chain network design by considering risk and robustness. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:79702-79717. [PMID: 34601678 PMCID: PMC8487343 DOI: 10.1007/s11356-021-16727-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 09/22/2021] [Indexed: 05/09/2023]
Abstract
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.
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Affiliation(s)
- Reza Lotfi
- Department of Industrial Engineering, Yazd University, Yazd, Iran.
- Behineh Gostar Sanaye Arman, Tehran, Iran.
| | - Bahareh Kargar
- School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Alireza Gharehbaghi
- Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
| | - Gerhard-Wilhelm Weber
- Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland
- IAM, METU, Ankara, Turkey
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10
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Torkayesh AE, Deveci M, Torkayesh SE, Tirkolaee EB. Analyzing failures in adoption of smart technologies for medical waste management systems: a type-2 neutrosophic-based approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:79688-79701. [PMID: 34554402 DOI: 10.1007/s11356-021-16228-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 08/25/2021] [Indexed: 04/16/2023]
Abstract
Medical waste management (MWM) systems are considered among the most important urban systems nowadays. Cities in different countries prefer to transform their infrastructure based on sustainability guidelines and practices. Meanwhile, smart technologies such as Internet of Things (IoT) and blockchain are being recently used in different urban systems of cities that aim to transform into smart cities. MWM systems are one of the main targets of integrating such smart technologies to maximize economic and social profits and minimize environmental issues. However, the transformation of traditional MWM systems into smart MWM systems and the adoption of such technologies can be a very resource-consuming task. One of the possible tasks in this process can be the identification of factors that cause failure in the adoption of smart technologies. Therefore, this study proposes a multi-criteria evaluation model based on type-2 neutrosophic numbers (T2NNs) to identify factors contributing to failure in the adoption of IoT and blockchain in smart MWM systems in Istanbul, Turkey. Results of the case study indicate that training for different stakeholders, market acceptance, transparency, and professional personnel are the main factors that lead to failure in the adoption of smart technologies. Training for different stakeholders, market acceptance, transparency, and professional personnel factors obtained distance values of 0.494, 0.381, 0.375, and 0.278, respectively, against the best factor which is security and privacy. In order to validate the results of the proposed approach, a sensitivity analysis test is performed. Results of this study can be useful for governmental and private MWM and green companies that are planning to adopt IoT and blockchain within their waste management (WM) system.
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Affiliation(s)
- Ali Ebadi Torkayesh
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, 34956, Istanbul, Turkey.
- School of Business and Economics, RWTH Aachen University, 52072, Aachen, Germany.
| | - Muhammet Deveci
- Department of Industrial Engineering, Turkish Naval Academy, National Defence University, 34940, Istanbul, Turkey
| | | | - Erfan Babaee Tirkolaee
- Department of Industrial and Systems Engineering, Istinye University, 34010, Istanbul, Turkey
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11
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Morjéne Y, Ndhaief N, Rezg N. Optimization of production batches in a circular supply chain under uncertainty. IFAC-PAPERSONLINE 2022; 55:1752-1757. [PMID: 38621012 PMCID: PMC9605719 DOI: 10.1016/j.ifacol.2022.09.651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
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.
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Affiliation(s)
| | | | - Nidhal Rezg
- LGIPM Laboratory, Lorraine University, Metz, France
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12
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Sun X, Yu H, Solvang WD. Towards the smart and sustainable transformation of Reverse Logistics 4.0: a conceptualization and research agenda. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:69275-69293. [PMID: 35972653 PMCID: PMC9378263 DOI: 10.1007/s11356-022-22473-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/06/2022] [Indexed: 06/12/2023]
Abstract
The recent advancement of digitalization and information and communication technology (ICT) has not only shifted the manufacturing paradigm towards the Fourth Industrial Revolution, namely Industry 4.0, but also provided opportunities for a smart logistics transformation. Despite studies have focused on improving the smartness, connectivity, and autonomy of isolated logistics operations with a primary focus on the forward channels, there is still a lack of a systematic conceptualization to guide the coming paradigm shift of reverse logistics, for instance, how "individualization" and "service innovation" should be interpreted in a smart reverse logistics context? To fill this gap, Reverse logistics 4.0 is defined, from a holistic perspective, in this paper to offer a systematic analysis of the technological impact of Industry 4.0 on reverse logistics. Based on the reported research and case studies from the literature, the conceptual framework of smart reverse logistics transformation is proposed to link Industry 4.0 enablers, smart service and operation transformation, and targeted sustainability goals. A smart reverse logistics architecture is also given to allow a high level of system integration enabled by intelligent devices and smart portals, autonomous robots, and advanced analytical tools, where the value of technological innovations can be exploited to solve various reverse logistics problems. Thus, the contribution of this research lies, through conceptual development, in presenting a clear roadmap and research agenda for the reverse logistics transformation in Industry 4.0.
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Affiliation(s)
- Xu Sun
- Department of Industrial Engineering, UiT-The Arctic University of Norway, Lodve Langesgate 2, 8514, Narvik, Norway
| | - Hao Yu
- Department of Industrial Engineering, UiT-The Arctic University of Norway, Lodve Langesgate 2, 8514, Narvik, Norway.
| | - Wei Deng Solvang
- Department of Industrial Engineering, UiT-The Arctic University of Norway, Lodve Langesgate 2, 8514, Narvik, Norway
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13
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Momenitabar M, Dehdari Ebrahimi Z, Arani M, Mattson J. Robust possibilistic programming to design a closed-loop blood supply chain network considering service-level maximization and lateral resupply. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-43. [PMID: 36157977 PMCID: PMC9483431 DOI: 10.1007/s10479-022-04930-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/24/2022] [Indexed: 05/04/2023]
Abstract
Reconfiguring the structure of the supply chain network is one of the most strategic and vital decisions in designing a supply chain network. In this study, a Closed-Loop Blood Supply Chain Network (CLBSCN) considering blood group compatibility, ABO-Rh(D), and blood product shelf life has been studied to determine the best strategic and tactical decisions simultaneously considering lateral resupply/transshipment and service-level maximization. Several vital parameters, including supply and demand, are considered fuzzy numbers to approximate reality due to the nature of the world. Furthermore, two crucial factors include ABO-Rh(D) and blood product shelf life considered, while the concept of lateral resupply governs the interconnections of hospitals' excess blood units. We propose a fuzzy multi-objective Mixed-Integer Non-Linear Programming (MINLP) model to consider two critical objective functions: minimizing the total costs of the network and maximizing the minimum service level to the patients at each Hospital. The fuzzy multi-objective MINLP model is converted to a deterministic multi-objective model using the equivalent auxiliary crisp model to deal with uncertainty. Then, by utilizing two interactive fuzzy solution approaches, the results have been compared based on a real case study to suggest the best solution for the proposed model. Also, we conduct sensitivity analysis on essential parameters such as demand, supply, and capacity to understand how these parameter variations impact two proposed objective functions. Then, the proposed model is tested on a real case study for model validation. The results confirmed that considering the lateral resupply could significantly save the costs of the designed network by a total of $343,000. Interestingly, maximizing the minimum service level at hospitals increased the service level from 58% to 68%.
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Affiliation(s)
- Mohsen Momenitabar
- Department of Transportation, Logistics, and Finance, College of Business, North Dakota State University, Fargo, ND 58105 USA
| | - Zhila Dehdari Ebrahimi
- Department of Transportation, Logistics, and Finance, College of Business, North Dakota State University, Fargo, ND 58105 USA
| | - Mohammad Arani
- Department of Systems Engineering, The University of Arkansas at Little Rock, Little Rock, AR 72204 USA
| | - Jeremy Mattson
- Department of Transportation, Logistics, and Finance, College of Business, North Dakota State University, Fargo, ND 58105 USA
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14
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Yin F, Zhao Y. Distributionally robust equilibrious hybrid vehicle routing problem under twofold uncertainty. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.140] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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15
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Mahyari KF, Sun Q, Klemeš JJ, Aghbashlo M, Tabatabaei M, Khoshnevisan B, Birkved M. To what extent do waste management strategies need adaptation to post-COVID-19? THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 837:155829. [PMID: 35561899 PMCID: PMC9087148 DOI: 10.1016/j.scitotenv.2022.155829] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/02/2022] [Accepted: 05/06/2022] [Indexed: 05/02/2023]
Abstract
The world has been grappling with the crisis of the COVID-19 pandemic for more than a year. Various sectors have been affected by COVID-19 and its consequences. The waste management system is one of the sectors affected by such unpredictable pandemics. The experience of COVID-19 proved that adaptability to such pandemics and the post-pandemic era had become a necessity in waste management systems and this requires an accurate understanding of the challenges that have been arising. The accurate information and data from most countries severely affected by the pandemic are not still available to identify the key challenges during and post-COVID-19. The documented evidence from literature has been collected, and the attempt has been made to summarize the rising challenges and the lessons learned. This review covers all raised challenges concerning the various aspects of the waste management system from generation to final disposal (i.e., generation, storage, collection, transportation, processing, and burial of waste). The necessities and opportunities are recognized for increasing flexibility and adaptability in waste management systems. The four basic pillars are enumerated to adapt the waste management system to the COVID-19 pandemic and post-COVID-19 conditions. Striving to support and implement a circular economy is one of its basic strategies.
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Affiliation(s)
- Khadijeh Faraji Mahyari
- Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Iran
| | - Qiaoyu Sun
- Center for Science and Technology Personnel Exchange and Development Service, Ministry of Science and Technology of the People's Republic of China, No.54 Sanlihe Road, Xicheng District, Beijing, PR China
| | - Jiří Jaromír Klemeš
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic
| | - Mortaza Aghbashlo
- Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Iran
| | - Meisam Tabatabaei
- Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries (AKUATROP), Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia; Henan Province Engineering Research Center for Biomass Value-added Products, School of Forestry, Henan Agricultural University, Zhengzhou 450002, China
| | - Benyamin Khoshnevisan
- Department of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Denmark.
| | - Morten Birkved
- Department of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Denmark.
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16
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Andoh EA, Yu H. A two-stage decision-support approach for improving sustainable last-mile cold chain logistics operations of COVID-19 vaccines. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-31. [PMID: 36035453 PMCID: PMC9392992 DOI: 10.1007/s10479-022-04906-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/02/2022] [Indexed: 05/06/2023]
Abstract
The COVID-19 pandemic has become a global health and humanitarian crisis that catastrophically affects many industries. To control the disease spread and restore normal lives, mass vaccination is considered the most effective way. However, the sustainable last-mile cold chain logistics operations of COVID-19 vaccines is a complex short-term planning problem that faces many practical challenges, e.g., low-temperature storage and transportation, supply uncertainty at the early stage, etc. To tackle these challenges, a two-stage decision-support approach is proposed in this paper, which integrates both route optimization and advanced simulation to improve the sustainable performance of last-mile vaccine cold chain logistics operations. Through a real-world case study in Norway during December 2020 and March 2021, the analytical results revealed that the logistics network structure, fleet size, and the composition of heterogeneous vehicles might yield significant impacts on the service level, transportation cost, and CO2 emissions of last-mile vaccine cold chain logistics operations.
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Affiliation(s)
- Eugenia Ama Andoh
- Department of Industrial Engineering, UiT The Arctic University of Norway, Lodve Langesgate 2, 8514 Narvik, Norway
| | - Hao Yu
- Department of Industrial Engineering, UiT The Arctic University of Norway, Lodve Langesgate 2, 8514 Narvik, Norway
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17
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Goodarzian F, Navaei A, Ehsani B, Ghasemi P, Muñuzuri J. Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: artificial intelligence-based solutions. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-45. [PMID: 35540307 PMCID: PMC9071011 DOI: 10.1007/s10479-022-04713-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/04/2022] [Indexed: 05/14/2023]
Abstract
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.
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Affiliation(s)
- Fariba Goodarzian
- Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, 11, 3rd Street NW, P.O. Box 2259, Auburn, WA 98071 USA
- Organization Engineering Group, School of Engineering, University of Seville, Camino de los Descubrimientos s/n, 41092 Seville, Spain
| | - Ali Navaei
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Behdad Ehsani
- Department of Decision Sciences, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 2A7 Canada
| | - Peiman Ghasemi
- Department of Logistics, Tourism and Service Management, German University of Technology in Oman (GUtech), Muscat, Oman
| | - Jesús Muñuzuri
- Organization Engineering Group, School of Engineering, University of Seville, Camino de los Descubrimientos s/n, 41092 Seville, Spain
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18
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Adoption of innovative strategies to mitigate supply chain disruption: COVID-19 pandemic. OPERATIONS MANAGEMENT RESEARCH 2022. [PMCID: PMC9042663 DOI: 10.1007/s12063-021-00222-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
COVID-19 pandemic outbreak caused supply chain (SC) disruption and threatened human life across the world, which could be mitigated through innovative strategies. Based on this scenario, this study examines the impact of COVID-19 on green practices, SC crisis mitigation strategies, smart technologies, and sustainable supply chain performance in the Pakistani manufacturing industry. Data was collected from Pakistani firms and employed structural equation modeling for testing hypotheses. The empirical results found that the COVID-19 pandemic is statistically related to green practices, SC crisis mitigation strategies, and smart technologies, while it harms sustainable supply chain performance. Moreover, green practices, SC crisis mitigation strategies, and smart technologies positively contribute to sustainable supply chain performance. The results of this study also confirmed the mediating role of green practices, SC crisis mitigation strategies, and smart technologies and moderating role organizational commitment in the context of a developing economy’s manufacturing industry. This study enhances awareness and understanding and contributes to the existing literature on verifying the link between COVID-19 pandemic and green practices, SC crisis mitigation strategies, and smart technologies to increase sustainable supply chain performance during a pandemic disruption in the Pakistani context. This study supports the managers of supply chain and manufacturing firms in adopting green practices and smart technologies. Also, it helps in the formation and successful implementation of SC crisis mitigation strategies during the crisis.
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19
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A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions. APPL INTELL 2022; 52:13435-13455. [PMID: 35370360 PMCID: PMC8958817 DOI: 10.1007/s10489-022-03334-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/02/2022] [Indexed: 11/25/2022]
Abstract
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.
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20
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A Cluster-based Stratified Hybrid Decision Support Model under Uncertainty: Sustainable Healthcare Landfill Location Selection. APPL INTELL 2022; 52:13614-13633. [PMID: 35280110 PMCID: PMC8898660 DOI: 10.1007/s10489-022-03335-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/02/2022] [Indexed: 12/23/2022]
Abstract
Nowadays, healthcare waste management has become one of the significant environmental, health, and social problems. Due to population and urbanization growth and an increase in healthcare waste disposals according to the growing number of diseases and pandemics like COVID-19, disposal of healthcare waste has become a critical issue. Authorities in big cities require reliable decision support systems to empower them to make strategic decisions to provide safe disposal methods with a prospective vision. Since inappropriate healthcare waste management systems would definitely bring up dangerous environmental, social, health, and economic issues for every city. Therefore, this paper attempts to address the landfill location selection problem for healthcare waste using a novel decision support system. Novel decision support model integrates K-means algorithms with Stratified Best-Worst Method (SBWM) and a novel hybrid MARCOS-CoCoSo under grey interval numbers. The proposed decision support system considers waste generate rate in medical centers, future unforeseen but potential events, and uncertainty in experts’ opinion to optimally locate required landfills for safe and economical disposal of dangerous healthcare waste. To investigate the feasibility and applicability of the proposed methodology, a real case study is performed for Mazandaran province in Iran. Our proposed methodology could efficiently deal with 79 medical centers within 4 clusters addressing 9 criteria to prioritize candidate locations. Moreover, the sensitivity analysis of weight coefficients is carried out to evaluate the results. Finally, the efficiency of the methodology is compared with several well-known methods and its high efficiency is demonstrated. Results recommend adherence to local rules and regulations, and future expansion potential as the top two criteria with importance values of 0.173 and 0.164, respectively. Later, best location alternatives are determined for each cluster of medical centers.
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21
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Vehicle Routing Optimization for Pandemic Containment: A Systematic Review on Applications and Solution Approaches. SUSTAINABILITY 2022. [DOI: 10.3390/su14042053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The global spread of the novel coronavirus (COVID-19) has accounted for many deaths. The effective containment of the current COVID-19 epidemic calls for a fast and sustainable delivery strategy to minimize the impact of this crisis. As such, this study aimed to conduct a comprehensive review of research on the vehicle routing problem (VRP) from a sustainable viewpoint during the pandemic and explore viable delivery solutions that may aid in the containment of the COVID-19 pandemic. Through a systematic review of the selected articles, four broad themes of pandemic containment measures from the delivery aspect were identified: efficient pharmaceutical delivery strategy, contactless distribution, sustainable waste transportation strategy, and isolated and quarantine vehicle scheduling. Following that, the methodology utilized to execute the containment measures were analyzed, research gaps were hightlighted, and possibilities for future studies were suggested. In summary, the goal of this research is to provide an overview of the literature on the application of VRPs in pandemic control and to assist academics and practitioners in learning more about the performance metrics, models, and solution techniques utilized in pandemic control delivery operations.
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22
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Peyvandi A, Majidi B, Peyvandi S, Patra JC, Moshiri B. Location-aware hazardous litter management for smart emergency governance in urban eco-cyber-physical systems. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:22185-22214. [PMID: 35002472 PMCID: PMC8721641 DOI: 10.1007/s11042-021-11654-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 09/21/2021] [Accepted: 09/28/2021] [Indexed: 06/14/2023]
Abstract
Smart city management is facing a new challenge from littered face masks during COVID-19 pandemic. Addressing the issues of detection and collection of this hazardous waste that is littered in public spaces and outside the controlled environments, usually associated with biomedical waste, is urgent for the safety of the communities around the world. Manual management of this waste is beyond the capabilities of governments worldwide as the geospatial scale of littering is very high and also because this contaminated litter is a health and safety issue for the waste collectors. In this paper, an autonomous biomedical waste management framework that uses edge surveillance and location intelligence for detection of the littered face masks and predictive modelling for emergency response to this problem is proposed. In this research a novel dataset of littered face masks in various conditions and environments is collected. Then, a new deep neural network architecture for rapid detection of discarded face masks on the video surveillance edge nodes is proposed. Furthermore, a location intelligence model for prediction of the areas with higher probability of hazardous litter in the smart city is presented. Experimental results show that the accuracy of the proposed model for detection of littered face masks in various environments is 96%, while the speed of processing is ten times faster than comparable models. The proposed framework can help authorities to plan for timely emergency response to scattering of hazardous material in residential environments.
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Affiliation(s)
- Amirhossein Peyvandi
- Department of Computer Engineering, Faculty of Engineering, Khatam University, Tehran, Iran
| | - Babak Majidi
- Department of Computer Engineering, Faculty of Engineering, Khatam University, Tehran, Iran
- Emergency and Rapid Response Simulation (ADERSIM) Artificial Intelligence Group, Faculty of Liberal Arts & Professional Studies, York University, Toronto, Canada
| | - Soodeh Peyvandi
- Business Intelligence, University of Applied Sciences Upper Austria, Steyr, Austria
| | - Jagdish C. Patra
- Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia
| | - Behzad Moshiri
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada
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23
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Goodarzian F, Ghasemi P, Gunasekaren A, Taleizadeh AA, Abraham A. A sustainable-resilience healthcare network for handling COVID-19 pandemic. ANNALS OF OPERATIONS RESEARCH 2022; 312:761-825. [PMID: 34642527 PMCID: PMC8497050 DOI: 10.1007/s10479-021-04238-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/10/2021] [Indexed: 05/12/2023]
Abstract
UNLABELLED In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and multi-echelon mixed-integer linear programming model for the current network is formulated and designed. Formulating a new MILP model to design a sustainable-resilience healthcare network during the COVID-19 pandemic and developing three hybrid meta-heuristic algorithms are among the most important contributions of this research. In order to estimate the values of the required demand for medicines, the simulation approach is employed. To cope with uncertain parameters, stochastic chance-constraint programming is proposed. This paper also proposed three meta-heuristic methods including Multi-Objective Teaching-learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. Since heuristic approaches are sensitive to input parameters, the Taguchi approach is suggested to control and tune the parameters. A comparison is performed by using eight assessment metrics to validate the quality of the obtained Pareto frontier by the heuristic methods on the experiment problems. To validate the current model, a set of sensitivity analysis on important parameters and a real case study in the United States are provided. Based on the empirical experimental results, computational time and eight assessment metrics proposed methodology seems to work well for the considered problems. The results show that by raising the transportation costs, the total cost and the environmental impacts of sustainability increased steadily and the trend of the social responsibility of staff rose gradually between - 20 and 0%, but, dropped suddenly from 0 to + 20%. Also in terms of the on-resiliency of the proposed network, the trends climbed slightly and steadily. Applications of this paper can be useful for hospitals, pharmacies, distributors, medicine manufacturers and the Ministry of Health. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s10479-021-04238-2.
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Affiliation(s)
- Fariba Goodarzian
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
- Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, 11, 3rd Street NW, P.O. Box 2259. Auburn, Washington, 98071 USA
| | - Peiman Ghasemi
- Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Angappa Gunasekaren
- School of Business Administration, Penn
State Harrisburg, Middletown, PA 17057-
4898 USA
| | - Ata Allah Taleizadeh
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Ajith Abraham
- Center for Artificial Intelligence, Innopolis University, Innopolis, Russia
- Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, 11, 3rd Street NW, P.O. Box 2259. Auburn, Washington, 98071 USA
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24
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Medical Waste Treatment Technologies for Energy, Fuels, and Materials Production: A Review. ENERGIES 2021. [DOI: 10.3390/en14238065] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
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.
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25
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Multi-Objective Optimization for Healthcare Waste Management Network Design with Sustainability Perspective. SUSTAINABILITY 2021. [DOI: 10.3390/su13158279] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Healthcare Waste Management (HWM) is considered as one of the important urban decision-making problems due to its potential environmental, economic, and social risks and damages. The network of the HWM system involves important decisions such as facility locating, inventory management, and transportation management. Moreover, with growing concerns towards sustainable development objectives, HWM systems should address its environmental and social aspects as well as its economic and technical characteristics. In this regard, this paper formulates a novel multi-objective optimization model to empower companies in making optimized decisions considering the economic, environmental, and social aspects. Within the proposed model, the first objective function aims to minimize the transportation costs, processing costs, and establishment costs. The second objective function aims to minimize environmental risks and emissions related to the transportation of waste between facilities. The third objective function aims to maximize job creation opportunities. Formulating these three functions, an Improved Multi-Choice Goal Programing (IMCGP) approach is proposed to solve the multi-objective optimization model, which is then compared with the Goal Attainment Method (GAM). Finally, to show the applicability and feasibility of the proposed model, an illustrative example of healthcare waste management is analyzed, and the results are discussed.
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