A novel data-driven patient and medical waste queueing-inventory system under pandemic: a real-life case study

Abstract

It is necessary to control patient congestion in medical centers during pandemics where medical demand grows rapidly. Also, managing generated medical waste is critical since pandemic waste can be a source of disease spread. Although some researchers have studied healthcare optimization in medical systems, there is still a lack of models simultaneously managing congestion in medical centers integrated with waste management using a new application of queueing systems. The model is also the first to use a data-driven method to develop a mathematical model of healthcare and waste management. To fill these gaps, this paper develops a multi-shift mathematical model to manage the congestion in the medical center and medical waste during the pandemic. To this aim, patients are categorized using machine learning algorithms at first. Then, the number of outpatients and inpatients, as well as medical waste, is modeled as a Markovian healthcare waste queueing-inventory system (HWQIS) using a bulk service queueing model. A case study based on the Covid-19 pandemic is applied after the model has been validated using twelve test problems. By determining the optimal size of waste packages, vehicle capacity, and the number of servers, we minimized the patients waiting time and reduced waste accumulation.

GRAPHICAL ABSTRACT

A hospital with outpatient and inpatient departments and waste system, along with their queueing models’ symbols.

Keywords:

• $M/M/C$ queue
• Bulk service $M/M^{\left[y\right]}/1$ queue
• Disaster management
• Healthcare optimization
• Medical waste management
• Machine learning

Data availability statement

The data supporting this study's findings are openly available in the article (Section 6).

Additional information

Notes on contributors

Mohammad Rahiminia

Mohammad Rahiminia holds an MSc degree from the Department of Industrial Engineering in the major of Logistics and Supply Chain Management at the University of Tehran. In his MSc study, he worked on the application of queueing theory in healthcare systems during a pandemic. He published several papers from his MSc work concerned about the era of the Covid-19 pandemic and disaster management.

Sareh Shahrabifarahani

Sareh Shahrabifarahani received her MSc degree from the Department of Industrial Engineering in the major of Logistics and Supply Chain Management at the University of Tehran. Her research focuses on the development of mathematical approaches applied to practical logistics and supply chain management. Currently, she works on designing real-world inventory routing and job shop scheduling models as a supply chain expert in the pharmaceutical industry.

Mohammad Alipour-Vaezi

Mohammad Alipour-Vaezi is a Ph.D. student of Industrial & Systems Engineering at Virginia Tech. He has more than 3 years of research experience with several research contributions to various scientific journals and conferences. He has published several papers in international professional journals such as Expert Systems with Applications, Multimedia Tools and Applications, Soft Computing, etc. His research interests can be indicated as Supply Chain Management, Healthcare Systems, Operations Research, and Data-Driven Decision-Making.

Amir Aghsami

Amir Aghsami is a Ph.D. in Industrial Engineering at the School of Industrial Engineering, Khaje Nasir Toosi University of Technology. He received his MS in Industrial engineering from University of Tehran, Iran. He is currently a senior research fellow at the School of Industrial and Systems Engineering, College of Engineering, University of Tehran. He has published more than 60 papers in international professional journals such as Socio-Economic Planning Sciences, Computer and industrial engineering, Expert Systems with Applications, Quality technology & quantitative management, Journal of Cleaner Production, IISE Transactions on Healthcare Systems Engineering, etc. His main scientific interests include queueing theory, stochastic process, operations research, healthcare optimization, queueing–inventory systems, mathematical modeling, supply chain management, disaster management, data mining, waste management, and inventory control.

Fariborz Jolai

Fariborz Jolai is a Professor of Industrial Engineering at the School of Industrial and Systems Engineering, College of Engineering, University of Tehran. He has published more than 300 papers in international journals, such as European Journal of Operational Research, International Journal of Production Research, International Journal of Production Economics, IISE Transactions on Healthcare Systems Engineering, International Journal of Management Science and Engineering Management, Journal of Cleaner Production, Applied Mathematical Modelling, Journal of Humanitarian Logistics and Supply Chain Management, etc. His current research interests are Supply chain management, Scheduling, transportation optimization, healthcare optimization, queueing theory, humanitarian logistics, supply chain management, and production planning optimization problems.