The crew re-scheduling problem (CRSP) is a critical challenge in the operation and management of urban rail transit (URT) systems, especially when restoring service after disruptions. When a crew member unexpectedly leaves duty due to emergency events like illness, the train assigned to that crew may get stranded in one operating direction, which will block the following trains operating in the same direction. To address this issue, this study first introduces a closed-loop driving strategy. This strategy reallocates limited crew resources across both operating directions to maintain the basic operations of the URT system during emergency periods. On this basis, an integer linear programming (ILP) model is developed to describe the dynamic adjustments of train departure times. Valid inequalities are incorporated to generate feasible crew task sets rapidly, and the proposed model is solved by using Gurobi. To meet the stringent time requirements for rescheduling during disruptions, an improved greedy algorithm is further designed to manage crew assignment under emergency conditions efficiently. Finally, the effectiveness of the proposed approach is evaluated through a real-world case study based on the Beijing urban rail transit network. The results demonstrate that the proposed model can respond rapidly within 30 min after an incident occurs. It not only limits the generation time of each crew task to within 1 min but also achieves a relative working balance between crews by combining short-duration tasks.