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Machine Learning and Optimization Methodologies in Railway Transportation Railway optimization problems are inherently complex due to various factors, such as safety requirements, capacity limitations, and large-scale problem sizes. This complexity makes precise modeling and obtaining accurate solutions highly challenging, often necessitating a trade-off between computational performance and solution quality. In recent years, the rapid development of Machine Learning (ML) has driven innovative research in railway transportation, bringing many advantages and solutions. The application of ML enables in-depth analysis and exploration of the massive data generated within railway systems, uncovering hidden patterns and trends. Moreover, ML can also overcome the limitations of traditional methods. For example, when combined with optimization methodologies, ML may optimize the decision-making process, provide an innovative and effective means to significantly enhance the accuracy, timeliness,
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