🤖 AI Summary
This study addresses the joint optimization of train departure sequencing and track allocation in short-term, high-frequency railway dispatching scenarios. A unified QUBO (Quadratic Unconstrained Binary Optimization) model is developed to simultaneously represent departure position assignment and en-route track selection. Innovatively, a simulation-based evaluation layer is integrated to account for dynamic operational factors—including block occupancy, intermediate station dwell times, platform congestion, service variability, and delay propagation. The proposed framework uniquely combines QUBO modeling with simulation-driven assessment, enabling fair benchmarking of classical heuristics, quantum-inspired methods, and hybrid algorithms—such as QPSO-QAOA—within a common platform. Experimental results demonstrate that, under dynamic conditions, quantum-enhanced algorithms achieve 4.28%–26.26% lower composite costs and reduce total delays by 4.37%–24.25% compared to classical approaches, confirming their superiority in complex railway scheduling environments.
📝 Abstract
This study examines the coordinated optimization of departure sequencing and section-track allocation in railway short-term concentrated departure scenarios. A quadratic unconstrained binary optimization (QUBO) model is formulated to represent departure-position assignment and section-track selection within a unified binary framework. Because the quality of a dispatching scheme depends on time-dependent operational interactions that cannot be fully captured by a static combinatorial model, a simulation-based evaluation layer is introduced to assess section occupation, intermediate-station waiting, platform-capacity pressure, running-time fluctuations, and delay propagation. Within this layered framework, conventional heuristics, quantum-inspired algorithms, and hybrid algorithms are compared on the same decision structure. The results show that the QUBO model can generate feasible candidate schemes after decoding, while the simulation layer clearly differentiates the operational performance of the competing algorithms under both normal and disturbed conditions. In the tested scenarios, QPSO-QAOA performs best under normal conditions, and the quantum-enhanced methods reduce comprehensive cost by 4.28\%--26.26\% and total delay by 4.37\%--24.25\% on average under dynamic conditions relative to their conventional counterparts. These findings suggest that the integration of QUBO-based modeling and simulation-based evaluation provides a useful methodological framework for railway short-term concentrated departure scheduling, although validation with real operational data remains necessary.