Quantum-Inspired Evolutionary Neighborhood Search for Arrival-Departure Track Utilization Adjustment under Short-Term Disturbances
2026-07-27 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors studied how to fix train delays at busy train stations after short disruptions. They created a model to better schedule train arrivals, track use, and departures by considering resource availability and costs. They tested their method, called QEA-NS, on data from a German station and found it reduced total train delays by about 25% compared to another approach. However, their method takes longer to compute solutions. Overall, the authors showed that their approach can improve delay recovery but needs faster computation.
train schedulingresource allocationtrain delaysevolutionary algorithmquantum-inspired algorithmstation resource managementarrival-departure adjustmentGTFS timetableCP-SAT solverneighbor search
Authors
Xiaobin Li, Wuming Lei, Yanbin Gao, Weiguang Wang
Abstract
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.