[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119577-en":3,"doc-seo-119577-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119577,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Machine Learning Applications for Delivery Time Prediction and Freight Planning","Rapid technological progress is transforming logistics and freight transportation, making transportation scheduling crucial for lowering costs, reducing delivery delays, and improving customer satisfaction. A central challenge is the Vehicle Routing Problem with Time Windows (VRPTW), which must optimize routes while strictly satisfying customer-specific timing constraints. Traditional optimization struggles with real-world complexity and dynamic uncertainty. This study presents a machine learning system that predicts transit time, availability time, and service time from historical and contextual data, enabling routing algorithms to schedule more accurately and adaptively. Building on Random Forest research, advanced preprocessing and feature engineering are used. Evaluation on real-world datasets demonstrates lower Mean Absolute Error (MAE) than commonly used machine learning models, supporting practical deployment and scalable, intelligent logistics planning.","DOI 10. 15622/ia.24.5.5  \nN.V. HUNG, T.T. HUONG, N. TAN, T.C. DOAN, N.N. HOANG  \nMACHINE LEARNING APPLICATIONS FOR DELIVERY TIME PREDICTION AND FREIGHT PLANNING  \nNguyen Viet Hung, Trinh Thu Huong, Nguyen Tan, Truong Cong Doan, Nguyen Nam Hoang Machine Learning Applications for Delivery Time Prediction and Freight Planning. Abstract. 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.  \nOverall, this study contributes a scalable and intelligent solution to a longstanding logistics problem, paving the way for more responsive and cost-effective transportation systems.  \nKeywords: Vehicle Routing Problem with Time Windows (VRPTW), machine learning models, logistics optimization, transit time prediction, random forest improvement, data processing techniques.  \n1. Introduction. The transportation industry plays a pivotal role in the global supply chain, especially as the volume of freight movement continues to surge in response to rising consumer demand and evolving market expectations. With the rapid growth of e-commerce, same-day delivery services, and global trade expansion, transportation systems are under increasing pressure to operate with high levels of precision and reliability. This intensifying demand places a significant burden on logistics providers, who must now balance efficiency, costeffectiveness, and punctuality while navigating various real-world constraints such as traffic congestion, driver availability, and unpredictable weather conditions. As a result, transportation companies are continuously seeking innovative solutions to maintain their competitive edge in a highly dynamic and time-sensitive environment.  \nAmong the most pressing challenges is the ability to meet strict delivery time requirements – a factor that directly impacts customer satisfaction and loyalty. Inaccurate delivery estimates can lead to missed time windows, increased operational costs, and reputational damage. To address these concerns, predictive analytics and optimization techniques have become essential tools. Specifically, the Vehicle Routing Problem with Time Windows (VRPTW) has emerged as a critical area of focus within logistics and","cbCaiimwcTy1VpFV","https://ap.wps.com/l/cbCaiimwcTy1VpFV","pdf",2129347,1,29,"English","en",105,"# Introduction\n## Transportation demand and delivery-time pressure\n## Vehicle Routing Problem with Time Windows (VRPTW)\n## Machine learning and predictive analytics approaches","[{\"question\":\"What problem does the study focus on?\",\"answer\":\"The study targets the Vehicle Routing Problem with Time Windows (VRPTW), aiming to find efficient delivery routes that satisfy time-window constraints for each customer while managing logistics complexity.\"},{\"question\":\"How does the proposed system improve VRPTW solutions?\",\"answer\":\"It uses machine learning to predict key time-related parameters—transit time, availability time, and service time—based on historical and contextual data, helping routing decisions become more informed and adaptable.\"},{\"question\":\"What modeling approach is emphasized in the paper?\",\"answer\":\"The work builds on prior Random Forest research and enhances it with advanced preprocessing techniques and feature engineering to improve prediction accuracy.\"}]","Machine Learning Applications for Delivery Time Prediction and Freight Planning | 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problem does the study focus on?","Question",{"text":75,"@type":76},"The study targets the Vehicle Routing Problem with Time Windows (VRPTW), aiming to find efficient delivery routes that satisfy time-window constraints for each customer while managing logistics complexity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed system improve VRPTW solutions?",{"text":80,"@type":76},"It uses machine learning to predict key time-related parameters—transit time, availability time, and service time—based on historical and contextual data, helping routing decisions become more informed and adaptable.",{"name":82,"@type":73,"acceptedAnswer":83},"What modeling approach is emphasized in the paper?",{"text":84,"@type":76},"The work builds on prior Random Forest research and enhances it with advanced preprocessing techniques and feature engineering to improve prediction 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