Application of Machine Learning Algorithms for Optimizing Document Workflow Management in Railway Freight Transportation

Abstract

Railway freight transportation is a crucial component of global logistics, requiring efficient and secure document workflow management. Traditional document processing methods are often time-consuming, error-prone, and inefficient. The rapid advancement of machine learning (ML) provides new opportunities to optimize document handling in railway freight systems. This study explores the application of ML algorithms, including classification, clustering, and natural language processing (NLP), to automate document workflow and improve operational efficiency. This study provides an example of embedding ML models in current railway freight management systems as one of the suggested system architectures. These experimental findings demonstrate incredibly high improvement rates in terms of efficiency, accuracy, speed, and error reduction from document processing. This implies that the efficiency gains of document handling procedures mechanized through the application of intelligent machines will positively affect the decision-making role, decrease labor intensity for operations personnel, and increase the overall effectiveness of the freight operation. Reinforcement learning and hybrid AI approaches may be potential areas of study in the future to enhance the system.