A Rolling Optimisation Approach for High-Speed Railway Crew Rostering

Abstract

High-speed railway operations rely on various crew members. Among them, onboard mechanics are responsible for monitoring train systems and handling in-transit faults, bearing long working hours and critical safety responsibilities. However, they have received limited attention in existing crew rostering research, particularly those residing outside the base depot city. Additionally, the crew rostering plan often requires adjustments during execution, due to foreseeable disruptions such as leave requests, training courses, and additional tasks. Yet, these practical issues are often ignored, with adjustments mainly made manually and without a systematic correction approach. To address these gaps, this paper first proposes a hybrid service pattern and a mathematical model to design more reasonable crew routes and provide more convenient task arrangements for onboard mechanics. Following this, a rolling optimisation model is introduced to address foreseeable disruptions during the execution of the rostering plan, aiming to minimise plan modifications while maintaining balanced workloads. The relationship between the two models is sequential, with the crew route information obtained from the first model serving as partial input for the second model. A real-world case study based on data from the Qingdao EMU Depot in China is conducted to validate the effectiveness of the proposed models, with Gurobi used for solving. The computational results show that the crew routes are well assessed and balanced in terms of both operational costs and work convenience. Furthermore, the rolling optimisation model achieves a significantly lower modification-to-disruption ratio than the manual approach, ensuring greater robustness against disruptions. It further improves workload balance, reduces commuting costs, and prevents overwork, offering a practical and efficient solution for real-world planning.