Title | ||
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Real-Time Optimization Strategy For Single-Track High-Speed Train Rescheduling With Disturbance Uncertainties: A Scenario-Based Chance-Constrained Model Predictive Control Approach |
Abstract | ||
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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. (C) 2020 Elsevier Ltd. All rights reserved. |
Year | DOI | Venue |
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2021 | 10.1016/j.cor.2020.105135 | COMPUTERS & OPERATIONS RESEARCH |
Keywords | DocType | Volume |
High-speed train, Train rescheduling, Scenario-based chance-constrained model predictive control, Disturbance uncertainties | Journal | 127 |
ISSN | Citations | PageRank |
0305-0548 | 3 | 0.38 |
References | Authors | |
0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zhimin Huang | 1 | 250 | 27.25 |
Shukai Li | 2 | 108 | 11.61 |
Yanhui Wang | 3 | 16 | 6.74 |
Yihui Wang | 4 | 7 | 2.73 |
Lixing Yang | 5 | 274 | 28.39 |