Due to the high frequency of departures, Urban Rail Transit (URT) has emerged as the top choice for transportation among numerous individuals. However, when there is a disruption, the shorter departure interval poses a huge challenge to train rescheduling. Disruption may cause large-scale train delays or even paralysis of the entire line operation, seriously affecting the travel of passengers. This paper conducts a thorough study on the train rescheduling problem during disruption in URT systems. We implement holding and short-turning strategies and propose a NIP model aimed at minimizing passenger travel costs and deviations from the original train timetable, with the objective of obtaining a better train rescheduling timetable in a short time. We propose an iterative optimization algorithm utilizing Adaptive Large Neighborhood Search (ALNS), and we perform a case study using operational data from the Chengdu metro to validate the efficacy of the train rescheduling timetable. The result indicates that our approach decreases passenger travel costs by 15.50% and reduces timetable deviations by 16.47% when compared to the initial train timetable.