A Combined Simulation-Optimization Approach for Robust Timetabling on Main Railway Lines
提出一种仿真与优化相结合的方法，通过最小化预测延误和旅行时间的加权和，对双线铁路时刻表进行社会经济最优调整，在瑞典西部干线实验中使延误和准点率显著改善。
Performance aspects such as travel time, punctuality, and robustness are conflicting goals of utmost importance for railway transports. To successfully plan railway traffic, it is therefore important to strike a balance between planned travel times and expected delays. In railway operations research, a lot of attention has been given to construct models and methods to generate robust timetables—that is, timetables with the potential to withstand design errors, incorrect data, and minor everyday disturbances. Despite this, the current state of practice in railway planning is to construct timetables manually, possibly with support of microsimulation for robustness evaluation. This paper aims to narrow the gap between the state-of-the-art optimization-based research approaches and the current state of practice to construct timetables by combining simulation and optimization. The paper proposes a combined simulation-optimization approach for double-track lines, which generalizes previous work to allow full flexibility in the order of trains by including a new and more generic model to predict delays. By utilizing delay data from simulation, the approach can make socioeconomically optimal modifications of a given timetable by minimizing predicted disutility—the weighted sum of scheduled travel time and total predicted delay. In a large simulation experiment on the heavily congested Swedish Western Main Line, it is demonstrated that compared with a real-life, manually constructed timetable, large reductions of delays as well as improvements in punctuality could be obtained for a small cost of marginally longer travel times. The cost of scheduled in-vehicle travel time and mean delay was reduced by 5% on average, representing a large improvement for a highly utilized railway line. Furthermore, a separate scaling experiment indicates that the approach can also be suitable for larger problems. Funding: This research was funded by Trafikverket [Grants TRV 2016/5090 and TRV 2020/72690].