Machine learning demonstrates improved accuracy in delivery scheduling for logistics, suggesting new solutions for the vehicle routing problem with time windows.
Key Points
Our machine learning model reduces delivery delays and improves customer satisfaction in logistics.
The framework outperforms traditional optimization methods in predicting key parameters like transit time.
By using real-world datasets, we validate the effectiveness of our machine learning approach in freight planning.
Our study confirms significant performance improvements, with a focus on minimizing mean absolute error in delivery time predictions.