Optimizing COVID-19 medical waste management using goal and robust possibilistic programming

Abstract

During the global Coronavirus Disease (COVID-19) pandemic, the exponential rise in Hazardous Medical Waste (HMW) due to increased demand for personal protective equipment and heightened medical requirements posed significant threats to public health. This study proposes an innovative approach using a reverse logistics supply chain network that comprehensively integrates sustainability factors (e.g., cost, working conditions, exposure risks, and environmental impact) to manage the risks associated with medical waste effectively amid the pandemic. This research focuses on employing a guideline-based allocation of medical waste to specific technologies, leveraging the Torabi-Hassini (TH), Lp-metric (Lebesgue metric), and Goal Attainment (GA) approaches and robust possibilistic programming to address uncertainties. A real-case study validates the proposed model, demonstrating its ability to balance multiple objectives by optimizing the flow among treatment centers and introducing new Temporary Treatment Centers (TTCs). Also, we analyze broad sensitivity through weights assigned to the objective functions to obtain Pareto solutions. The convexity of the Pareto front confirms the conflict among the objective functions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach specifies that the Lp-metric approach outperforms the others, and the TH approach is regarded as the second rank. The study's findings highlight the model's efficacy and provide crucial managerial insights for health organization administrators in efficiently managing the HMW supply chain network.