Quantum Algorithm Optimizes Train Track Allocation Under Disturbances

A new study from arXiv introduces a quantum-inspired evolutionary algorithm combined with neighborhood search (QEA-NS) to adjust arrival-departure track utilization under short-term disturbances at major passenger railway stations. The research models station resources as zone-level resource-occupation intervals and formulates a track allocation adjustment model that imposes resource compatibility as a feasibility condition while jointly considering train delays and resource reassignment costs. Perturbation instances were constructed using GTFS timetable data from Frankfurt Hauptbahnhof, Germany. The proposed QEA-NS method was compared with CP-SAT under the same candidate resource sets. The study addresses the need for coordinated adjustment of track allocation, station resource occupation, and train retiming to recover from short-term disturbances that alter train arrival and departure times and resource release sequences.