Joint Optimization Method for Preventive Maintenance and Train Scheduling of Subway Vehicles Based on a Spatiotemporal Network Graph

To address the challenges posed by the interdependence between subway vehicle scheduling and maintenance planning, which complicates joint optimization modeling and solution processes, this study proposes a spatiotemporal network-based joint optimization method for subway vehicle preventive maintenance and scheduling. First, based on spatiotemporal network theory, the transition process between train operation scheduling and preventive maintenance states is analyzed, and a spatiotemporal state network graph is constructed to represent the temporal and spatial transitions of subway vehicles throughout the planning period. The vehicle’s operational workflow is represented as a path within this network. Next, leveraging the generated spatiotemporal network path set, a joint optimization model for preventive maintenance and scheduling is formulated, integrating optimization objectives and constraints to achieve coordinated optimization. Finally, an improved genetic algorithm is employed to solve the model and determine the optimal scheduling and maintenance strategy. The experimental results demonstrate that the proposed method effectively addresses the challenges in modeling and solving the joint optimization problem, enabling efficient coordination between maintenance and scheduling while enhancing the overall operational efficiency in subway vehicle management.