Circular economy application in designing sustainable medical waste management systems

Medical waste management (MWM) system is one of the most important issues in the management of medical services and entails a high cost for those in charge of collecting and disposing of this type of hazardous materials. With increasing social awareness and greater knowledge of health and environmental issues, reducing raw material reserves and energy shortages along with increasing demand and urbanization led urban planners to find a way to optimally manage such wastes. Achieving this goal requires a comprehensive and holistic view of all the factors affecting MWM. Meanwhile, medical waste composition plays a significant role in choosing the efficient method for MWM.

On the other hand, countries are moving from a linear economy to a circular economy. This shift has reformed the current MWM development as well as industrial and economic developments. In a context of increasing pressure on strategic resources, the circular economy suggests to move away from using unique materials towards the application of waste materials to have an economic model that limits environmental impacts. Sustainable development, however, tries to keep tracking social aspects. Accordingly, there is a close relationship between MWM, circular economy, and sustainable development.

To address the above-mentioned requirements, this special issue entitled “Circular Economy Application in Designing Sustainable Medical Waste Management Systems” received 22 manuscripts between May 4, 2021, and September 4, 2021. Most of the submissions fell into the aforementioned research topics of this special issue. Particularly, seven manuscripts were accepted for publication in the Journal of Environmental Science and Pollution Research after providing the requested revisions by the authors where the average acceptance rate was 31.8%.

The published manuscripts thoroughly investigated the application of optimization and multi-criteria decision-making (MCDM) to bring together current progress on the circular economy, sustainable development, and novel models and solution techniques which can contribute to a better understanding of the performances of MWM systems and provide useful practical strategies. The details of contributions are as follows:

• An effective dynamic immune optimization control was examined by Li et al. (2021) for the wastewater treatment process.

• A novel MCDM approach based on Failure Modes and Effects Analysis (FMEA) models were suggested by Jafarzadeh Ghoushchi et al. (2022) to design sustainable MWM systems.

• Risk and robustness of the MWM were evaluated through designing a viable waste chain network by Lotfi et al. (2021).

• Mei et al. (2021) studied the disposal capacity bottlenecks under a novel coronavirus pneumonia outbreak to optimize a medical waste recycling network.

• A time series model and Genetic Algorithm (GA) were employed by Safaei et al. (2022) to configure a multi-echelon multi-period closed-loop supply chain (CLSC) network focusing on the role of waste management.

• Torkayesh et al. (2021) proposed a type-2 neutrosophic-based MCDM approach to analyze the failures in adoption of smart technologies for MWM systems. A review on water simulation models was conducted by Soleimanian et al. (2022) to address the nexus of water, energy, and food (WEF) along with waste treatment.

Biographies

Erfan Babaee Tirkolaee

obtained a BSc. (2012) and MSc. (2014) in Industrial Engineering from Isfahan University of Technology in Isfahan, Iran. Then, he received a Ph.D. degree (2019) in Industrial Engineering from Mazandaran University of Science and Technology in Babol, Iran. Dr. Erfan Babaee Tirkolaee is currently an assistant professor in the Department of Industrial Engineering at Istinye University in Istanbul, Turkey. Meanwhile, he has worked as a Quality Assurance consultant and training manager in some automotive industries in Iran and could go through different relevant courses like ISO 9001: 2015 and IATF 16949–2016. He has been verified as a scientific elite by the Young Researchers and Elite Club, Islamic Azad University in 2017 and Iran’s National Elites Foundation in 2018. He has published more than 70 papers in high-quality journals and conferences and has been serving as a reviewer in many reputed journals such as IEEE Transactions on Fuzzy System, Journal of Cleaner Production, Waste Management, Computers & Industrial Engineering, and Optimization. Erfan Babaee Tirkolaee has been recognized as Top Peer Reviewer in 2 of the Essential Science Indicators research areas by Publons. Moreover, he is a member of the editorial/guest editorial board in several journals such as Annals of Operations Research, Environmental Science and Pollution Research, Management Decision, and International Journal of Supply and Operations Management. His research fields include waste management, supply chain management, transportation/routing problems, solution algorithms, and uncertainty-handling techniques.

Alireza Goli

is currently an assistant professor of the Department of Industrial Engineering and Future Studies Faculty of Engineering at the University of Isfahan, Iran. He received his Bachelor and Master Degree in Industrial Engineering from Golpayegan University of Technology (Iran, 2013) and Isfahan University of Technology (Iran, 2015), respectively. Then, he received a Ph.D. degree in Industrial Engineering from Yazd University (Iran, 2019). He has published more than 60 papers in high-quality journals and conferences and has been serving as a reviewer in many reputed journals such as IEEE Transactions on Fuzzy System, Journal of Supercomputing, and Annals of Operations Research. He has reached an excellent reviewer in Publons in 2019. He has been working as Guest Editor in some reputable journals like Environment, Development and Sustainability, and Environmental Science and Pollution Research. In addition, he has been serving as a reviewer in many reputed journals such as Supercomputing, IEEE Transactions on Fuzzy System, and expert system with application. He is working as a member of the editorial board in different journals like the Journal of Applied Research in Industrial Engineering and the International Journal of Applied Optimization Studies. His current research interests include supply chain management, disaster relief optimization, meta-heuristic algorithms, robust optimization, artificial intelligence, and portfolio management.

Seyedali Mirjalili

is a Professor at Torrens University Center for Artificial Intelligence Research and Optimization and internationally recognized for his advances in nature-inspired Artificial Intelligence (AI) techniques. He is also the founding director of the Centre for Artificial Intelligence Research and Optimization at Torrens University Australia. He is internationally recognized for his advances in Optimization and Swarm Intelligence, including the first set of algorithms from a synthetic intelligence standpoint — a radical departure from how natural systems are typically understood — and a systematic design framework to reliably benchmark, evaluate, and propose computationally cheap robust optimization algorithms. Ali has published over 300 publications with over 40,000 citations and an H-index of 70. As the most cited researcher in Robust Optimization, he has been on the list of 1% highly cited researchers and named as one of the most influential researchers in the world by Web of Science for three consecutive years since 2019. In 2020, he was ranked 21st across all disciplines and 4th in Artificial Intelligence & Image Processing in the Stanford University’s list of World’s Top Scientists. In 2021, The Australian newspaper named him as the top researcher in Australia in three fields of Artificial Intelligence, Evolutionary Computation, and Fuzzy Systems. Ali is a senior member of IEEE and an associate editor of several AI journals including Neurocomputing, Applied Soft Computing, Advances in Engineering Software, Computers in Biology and Medicine, Healthcare Analytics, Applied Intelligence, and IEEE Access. His research interests include optimization, swarm intelligence, evolutionary algorithms, and machine learning.