Designing a resilient reverse network to manage the infectious healthcare waste under uncertainty: A stochastic optimization approach

Abstract

The global expansion of healthcare facilities has resulted in increased levels of infectious-hazardous waste, posing serious threats to the environment and public health. Existing waste management systems can become overwhelmed during health crises, such as epidemics or natural disasters, exacerbating the problem. This study formulates a mixed-integer linear programming model for developing a resilient infectious waste management reverse network during the outbreak of the COVID-19 pandemic. Health crisis are unpredictable in nature and it is almost impossible to predict their exact behavior. Therefore, in this paper, the uncertainty of ambiguous parameters is considered using a scenario-oriented approach. To make the proposed model resilient, three strategies, including establishing new collection centers, overtime, and cooperation with third-party logistics, are introduced. The results derived from running the developed model in GAMS software using the case study data showed that the active collection center cannot serve the network alone, and to make the network more resilient, the strategy of establishing a new collection center should be selected. The findings validate the model’s utility in designing a resilient waste management network during a health crisis, emphasizing the importance of resilience in infectious healthcare waste management networks.