Machine learning applications for delivery time prediction and freight planning

The rapid advancement of technology has a profound impact on logistics and freight transportation. Efficient management of transportation schedules is vital for businesses seeking to minimize costs, reduce delivery delays, and improve customer satisfaction. One of the most important challenges in this field is the Vehicle Routing Problem with Time Windows (VRPTW), which requires not only finding optimal delivery routes but also adhering to specific timing constraints for each customer or delivery point. Traditional optimization methods often struggle with the complexity and dynamic nature of real-world logistics, particularly when dealing with large-scale datasets and unpredictable factors such as traffic congestion or weather conditions. To address these limitations, this study introduces a machine learning-based system that enhances the performance of existing VRPTW solutions. Unlike conventional approaches that rely solely on heuristics or static planning, our system employs modern machine learning models to predict key time-related parameters – including transit time, availability time, and service time – based on historical and contextual data. These predictive capabilities allow the routing algorithms to make more informed decisions, resulting in more accurate and adaptable scheduling. Building on previous research involving Random Forest models, we propose a more robust framework that incorporates advanced preprocessing techniques and feature engineering to improve model accuracy. By training and evaluating the system using real-world datasets, we are able to simulate practical scenarios and validate the effectiveness of our approach. Experimental results show that our proposed method consistently outperforms other commonly used machine learning models in terms of Mean Absolute Error (MAE), thus confirming its potential for real-world applications. Overall, this study contributes a scalable and intelligent solution to a longstanding logistics problem, paving the way for more responsive and cost-effective transportation systems.