Real-time optimization strategy for single-track high-speed train rescheduling with disturbance uncertainties: A scenario-based chance-constrained model predictive control approach

Abstract

To improve the operational efficiency of high-speed railway system with disturbance uncertainties, a real-time optimization rescheduling strategy is designed based on the updated information for single-track high-speed railway system in this paper. Based on the characteristics of high-speed railway lines, a mixed-integer linear optimization model is constructed, where the decision variables involve the arrival times, departure times, arrival orders, departure orders and dwelling plans. Furthermore, to satisfy real-time requirements and to enhance the robustness of solutions, a scenario-based chance-constrained model predictive control (SC-MPC) algorithm is designed for solving the train rescheduling problem. Under the designed algorithm, the original linear model is converted to a non-linear mixed-integer model. To reduce the computational burden, the nonlinear model is converted to a linear mixed-integer model by a linearization method. The proposed strategy is compared with several typical benchmark strategies via a case study on the Beijing-Shanghai high-speed railway line. The simulation results show that the train delays can be effectively reduced by the proposed strategy and the rescheduling timetable has a good robustness.