Disruptions to high-speed train operations result in deviations from the official timetable and train delays, leading to scheduling conflicts. A conflict-free rescheduled timetable and corresponding executable speed profiles are essential to restore disrupted train operations. In this paper, we introduce an integrated framework for the train rescheduling and speed management problem (TRSMP) during disruptions caused by partial segment blockages. Under a flexible rescheduling strategy, affected trains can switch to the opposite track to bypass the blockage. A mixed-integer linear programming model is formulated to simultaneously optimize train times, orders, routes, and speed profiles, aiming to minimize the total deviation time and traction energy consumption. To reduce the complexity of the integrated model, we decompose the model into a reformulated integer programming model and a series of train operation plans. This decomposition approach transforms the TRSMP into a train plan selection problem, effectively decoupling both the interactions among trains and interdependencies between rescheduling and speed management. Moreover, we develop an adaptive algorithm with acceleration techniques to speed up the solving of the reformulated model. Case studies based on the Chinese high-speed railway show that our decomposition approach provides near-optimal solutions within acceptable computational time in large-scale instances. Our integrated method outperforms the sequential optimization method in both solution time and feasibility guarantees of speed profiles. The proposed rescheduling strategy exhibits enhanced effectiveness in mitigating delays while preserving line capacity during partial blockages. Funding: This research was supported by the National Natural Science Foundation of China Grants 72288101, 72331001, 72571152, 72322002, and U2469201, and the 111 Center Grant B20071. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0137 .
Shi et al. (Fri,) studied this question.