AI 中文总结
研究针对短期干扰下的到发轨道利用调整问题，提出结合邻域搜索的量子启发式进化算法QEA-NS，通过构建模型求解，与CP-SAT对比，结果显示该算法能提高恢复计划延误性能，但计算效率需提升。
AI 中文摘要
主要客运站的短期干扰会改变列车到发时间和车站资源释放顺序。本研究将列车到发、轨道占用和出发作业中的车站资源表示为区域级资源占用区间，建立了到发轨道分配调整模型，以资源兼容性为可行性条件，综合考虑列车延误和资源重新分配成本。提出了一种结合邻域搜索的量子启发式进化算法（QEA-NS）来求解该模型。利用德国法兰克福主火车站的GTFS时刻表数据构建扰动实例，将QEA-NS与CP-SAT在相同候选资源集和可行性标准下进行比较。两种方法都能生成满足资源兼容性约束的解决方案。QEA-NS的总延误为388分钟，而CP-SAT为519分钟，减少了25.2%。在10个随机扰动实例中，QEA-NS在每种情况下的总延误都更低。结果表明，在所采用的资源表示和约束下，QEA-NS提高了恢复计划的延误性能，但其计算效率有待进一步提高。
英文摘要
Short-term disturbances at major passenger railway stations alter train arrival and departure times as well as the release sequence of station resources. Effective recovery therefore requires coordinated adjustment of arrival-departure track allocation, station resource occupation, and train retiming. This study represents the station resources involved in train arrival, track occupancy, and departure operations as zone-level resource-occupation intervals. An arrival-departure track allocation adjustment model is formulated. Resource compatibility is imposed as the feasibility condition, while train delays and resource reassignment costs are jointly considered. A quantum-inspired evolutionary algorithm combined with neighborhood search (QEA-NS) is proposed to solve the model. Perturbation instances are constructed using GTFS timetable data from Frankfurt Hauptbahnhof, Germany. QEA-NS is compared with CP-SAT under the same candidate resource set and feasibility criteria. Both methods generate solutions satisfying the modeled resource compatibility constraints. QEA-NS yields a total delay of 388 min, compared with 519 min for CP-SAT, representing a reduction of 25.2\%. The mean delay of delayed trains decreases from 4.99 to 3.73 min, although QEA-NS requires a longer solution time. Across 10 random perturbation instances, QEA-NS achieves lower total delay in every case. Its mean total delay and standard deviation are 390.5 min and 35.945 min, respectively, compared with 673.8 min and 105.739 min for CP-SAT. The results indicate that, under the adopted resource representation and constraints, QEA-NS improves the delay performance of recovery plans. Its computational efficiency, however, requires further improvement.
