Optimization of Waste Recycling Vehicle Routing in Public Health Emergencies

With the frequent occurrence of public health emergencies such as the COVID-19 pandemic, the allocation and coordinated use of emergency supplies around the world have generated large amounts of waste, thus creating an urgent need to improve and optimize emergency reverse logistics systems. This study proposes a model for optimizing waste recycling vehicle routing to address waste management issues. The model provides ideal transportation solutions for the simultaneous collection of general waste and medical waste. Based on the characteristics of the model, an artificial intelligence algorithm—i.e., an improved genetic algorithm based on bit-string-matching coding and a field restriction operator—is designed and compared with an ant colony optimization algorithm. A synthetic numerical example inspired by the situation in Wuhan, China, is tested using relevant data on the locations of 18 medical facilities mentioned in existing case studies of the COVID-19 pandemic to verify the effectiveness and practicality of the model and the algorithm. This study provides support for decision-making regarding optimization strategies for the reverse logistics of waste generated during public health emergencies.