Multi-objective optimization for risk mitigation of medical waste disposal reverse logistics network

Given the serious risks medical waste poses to the environment and public health, this paper proposes a multi-objective optimization model to address the location-allocation problem within its reverse logistics network. We design an efficient reverse logistics network for medical waste disposal, featuring treatment centers equipped with incineration-melting technology, and develop a multi-objective optimization model that considers infectious risk, environmental risk, social risk, and total costs to solve the location-allocation problem within the proposed network. To support risk mitigation and cost reduction for the decision maker without prior preference information, we use Monte Carlo simulation to examine the impact of weighting factors on the model's outcomes. The proposed methodology is then applied to a real-world case study in Chongqing, China, to evaluate its applicability and effectiveness. The simulation results demonstrate that the centers in the proposed network are effectively located and medical waste is appropriately allocated across various scenarios, achieving minimum values of 3120.5 for infectious risk, 8.6 for environmental risk, 198.5 for social risk, and 210,617.9 CNY for total costs. Furthermore, the proposed model strikes a balance between risk mitigation and cost reduction, minimizing medical waste management risks without incurring excessive costs and achieving cost savings without compromising risk control efforts.