Enhancing Fairness in High-Speed Railway Crew Scheduling: A Two-Stage Heuristic Optimization Framework Under Daily-Adjusted Timetables

The existing crew base assignment system in high-speed railway operations struggles to cope with the frequent deployment of additional and coupled trains under the “One-Day-One-Operation Plan” dynamic scheduling paradigm. This often results in unequal overtime distribution among crews, low scheduling efficiency, and limited operational adaptability. To address the above-mentioned application challenges, this study proposes a shift from the fixed crew-based system towards a fully flexible pool-based system. Specifically, we develop a novel integer programming model designed to optimize monthly crew schedules with the primary objective of balancing total working hours across all crew teams. In this model, crew teams are treated as unified entities but are no longer permanently tied to specific train services. Instead, they are dynamically allocated to all available train tasks within the network. Numerical results, based on a real-world case study from Shanghai, China, demonstrate that the proposed model effectively automates the scheduling process. It significantly enhances fairness in working hour distribution while fully complying with all operational rules. Furthermore, by enabling crews to undertake a diverse range of services, the model substantially improves the flexibility of human resource allocation and the overall robustness of the crew management system. This research provides an efficient and scientific decision-support tool for tackling crew scheduling difficulties in dynamic railway operations.