The high-speed railway system often encounters inevitable disturbances due to adverse weather and equipment failures. In orderssss to achieve integrated train timetable and vehicles rescheduling under disturbance and restore normal operation of the high-speed railway system as soon as possible, an integer linear programming (ILP) model based on a time-space-state network is proposed. This model can avoid train cancellations, add additional stops for trains and adjust the train sequence through overtaking. It allows for flexible changes when the types of vehicle used by train are the same. In order to solve the model, the model predictive control (MPC) framework is used to decompose large-scale problems into several small-scale problems, while ensuring the real-time nature of the solution process. Within each time period, the Lagrangian relaxation algorithm is used to solve small-scale problems. Verify the effectiveness of the model and algorithm using actual data from the Beijing Zhangjiakou high-speed railway. Compared with commercial solvers, algorithms can reduce solving time while ensuring solution quality. Compared to fixed vehicle circulation, flexible circulation can effectively reduce the total delay of trains.