delay forecasting

This paper presents a method for predicting delivery delays and optimizing routes in logistics systems using Graph Neural Networks (GNNs). Modern logistics networks face numerous challenges due to unpredictable delays caused by dynamic traffic conditions, weather events, vehicle malfunctions, and other external factors. Traditional machine learning methods, such as regression models or decision trees, often prove inadequate in modeling such complex spatiotemporal dependencies inherent in logistical environments.