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

• Published in: Applied soft computing Vol. 142; p. 110372
• Main Authors: Nosrati-Abarghooee, Saeede, Sheikhalishahi, Mohammad, Nasiri, Mohammad Mahdi, Gholami-Zanjani, Seyed Mohammad
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 01.07.2023
• Subjects: Epidemic disruptions; Fuzzy goal programming; Healthcare waste management; Monte-Carlo simulation; Healthcare waste management; Fuzzy goal programming; Monte-Carlo simulation; Epidemic disruptions
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2023.110372

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. •Developing a bi-objective MILP model for healthcare waste management under uncertainty and epidemic disruptions.•Applying a Monte Carlo-based simulation approach to determine the amount of waste generated.•Employing fuzzy goal programming method to solve proposed bi-objective model.