An efficient column generation approach for crew re-scheduling and recovery in urban rail transit systems under emergency conditions

Abstract

Crew Re-Scheduling Problem is a significant challenge in urban rail transit systems, particularly when addressing service disruptions and restoring operational order. When crew members unexpectedly sign off due to emergencies (e.g., illness), the train assigned to their operation task may be stranded in one running direction. This can subsequently cause obstructions for trains following in the same direction, thereby impacting normal operations. To address this issue, we first propose introducing a closed-loop scheduling mode, which involves rearranging the finite crew members across both running directions to sustain operations during emergency periods. Subsequently, a Crew Re-Scheduling and Recovery (CRSRP) model is developed to response the depart-time changes of trains. To solve the model, a generic framework of column generation (CG) embedded labeling algorithm is re-engineered to meet re-scheduling time requirements and permit changes in running directions at disrupted stations, which could be adopted in different emergency phases. It is important to note that after fireman crews are supplemented, all crew members resume normal operations, but emergency tasks must still be prioritized. A greedy algorithm is devised to manage assignments during the recovery phase. Finally, a real-life case study from Beijing is presented to assess the effectiveness of the proposed method. The model demonstrates the capability to respond swiftly within 30 min post-accident and control the generation time of individual tasks within 1 min. Additionally, the fluctuation range of crew members’ scheduling time has been reduced to [4, 21] minutes. This evidence underscores the model’s efficacy in restoring operational order under emergency conditions.