🤖 AI Summary
This study addresses the scheduling disruptions in large passenger railway stations caused by short-term perturbations that alter train arrival/departure times and the sequence of station resource releases. To tackle this challenge, the authors propose an optimization framework integrating a quantum-inspired evolutionary algorithm (QEA) with neighborhood search (NS). The approach models station resources as zone-level occupancy intervals and formulates a track reassignment model that enforces resource compatibility constraints while jointly minimizing total train delay and resource reallocation costs. Experimental results on perturbation scenarios derived from GTFS timetables demonstrate that, within a unified feasible solution space, the proposed method consistently outperforms the CP-SAT solver across ten test instances, reducing average total delay from 673.8 to 390.5 minutes (a 42% improvement) and decreasing per-train average delay from 4.99 to 3.73 minutes, thereby significantly enhancing disruption recovery performance.
📝 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.