Design of urban medical waste recycling network considering loading reliability under uncertain conditions

The ongoing global coronavirus pandemic (COVID-19) has significantly increased urban medical waste. Such waste often contains pathogenic microorganisms, harmful chemicals, and even radioactive and defective sub-stances, hence imposing disease transmission and public health risks. Nevertheless, due to multiple factors, the amount of medical waste produced in medical institutions is stochastic. This paper proposed an optimization model for a waste recycling network consideration of loading reliability to minimize the collective cost of location, vehicle usage, and transportation. A modified ant colony algorithm combined with the K-means clus-tering method based on a genetic algorithm is then proposed (MACO-GKA) to solve the optimal location problem and the vehicle routing problem (LP-VRP). The numerical examples are then conducted in Xuzhou City, China to evaluate the performance of the proposed model. Taking the loading reliability level & theta; = 0.9 as an example, the results show that the total cost will reach $100546.53 when collection points are not set, but decrease to $86,907.11 when they are set. In the latter case, the total cost was reduced by 13.57%. The detailed results indicate that the selection and establishment of medical waste collection points are essential factors in designing an urban medical waste recycling network. The proposed MACO-GKA algorithm also outperforms the CPLEX solver.

Automated summary

The global coronavirus pandemic has led to a significant increase in urban medical waste, posing risks to disease transmission and public health. This study proposes an optimization model considering loading reliability to minimize the overall cost of medical waste recycling. The proposed MACO-GKA algorithm shows better performance compared to the CPLEX solver.