Collaborative optimization of rescue operation and timetable rescheduling under metro train failure. Issue 1 (31st December 2023) - Zhu, Qiaozhen; Bai, Yun; Yang, Lingling; Chen, Yao; Yan, Dongyang
Abstract : Train failures may lead to disruptions of train operations and degradation of the service quality. This situation requires a quick turnaround to rescue the disabled train and reschedule the timetable. This paper proposes a collaborative model to optimise train rescue operations and timetable rescheduling, which includes retiming, cancelling trains, skip-stopping, and inserting additional trains. The model objective is to minimise train rescue duration and the number of stranded passengers. A tabu search algorithm combined with solver GUROBI is designed to solve the model. Case studies show that the proposed method reduces the number of stranded passengers by 28.6% at the cost of 5% increment of train rescue duration compared to the sequential method which finds the rescue operations first and then reschedules the train timetable. Moreover, the results indicate that inserting additional trains and skip-stopping are effective in improving service quality during disruptions.