A Hybrid Optimization Approach Combining Rolling Horizon with Deep-Learning-Embedded NSGA-II Algorithm for High-Speed Railway Train Rescheduling Under Interruption Conditions

This study discusses the issue of train rescheduling in high-speed railways (HSR) when unexpected interruptions occur. These interruptions can lead to delays, cancellations, and disruptions to passenger travel. An optimization model for train rescheduling under uncertain-duration interruptions is proposed. The model aims to minimize both the decline in passenger service quality and the total operating cost, thereby achieving sustainable rescheduling. Then, a hybrid optimization algorithm combining rolling horizon optimization with a deep-learning-embedded NSGA-II algorithm is introduced to solve this multi-objective problem. This hybrid algorithm combines the advantages of each single algorithm, significantly improving computational efficiency and solution quality, particularly in large-scale scenarios. Furthermore, a case study on the Beijing–Shanghai high-speed railway shows the effectiveness of the model and algorithm. The optimization rates are 16.27% for service quality and 15.58% for operational costs in the small-scale experiment. Compared to other single algorithms or algorithm combinations, the hybrid algorithm enhances computational efficiency by 26.21%, 15.73%, and 25.13%. Comparative analysis shows that the hybrid algorithm outperforms traditional methods in both optimization quality and computational efficiency, contributing to enhanced overall operational efficiency of the railway system and optimized resource utilization. The Pareto front analysis provides decision makers with a range of scheduling alternatives, offering flexibility in balancing service quality and cost. In conclusion, the proposed approach is highly applicable in real-world railway operations, especially under complex and uncertain conditions, as it not only reduces operational costs but also aligns railway operations with broader sustainability goals.