Optimization of disruption management strategies for urban rail using variable neighborhood search algorithm

The occurrence of disruptions in urban rail cause train delays, extra passenger waiting times, and drop in service level and schedule adherence. Rail disruption management refers to the implementation of the control strategies to manage unexpected events and improve the system performance. In this study, a temporary line blockage is regarded as a source of disruption in train movements. A simulation-based optimization approach is proposed to minimize the total average waiting times and traveling times of passengers. Disruption management strategies include skip-stop and short-turn control policies. Simulation technique is used to model random variables in question, including train traveling time, random arrival rate of passengers, and the stochastic recovery time. The proposed approach also includes the variable neighborhood search algorithm to find the near optimal solutions. In order to validate the proposed methodology, several disruption scenarios in Tehran Metro Line 1 are investigated. The results of solving the real problems show the efficiency and proper use of simulation-based optimization method in the decision-making under disruption. The results have also shown that a combination of skip-stop and short-turn strategies gives travelers greater improvements in reducing the waiting and travelling times. The implementation of variable neighborhood search method on the real-world case study demonstrate that the combined strategy reduces the average traveling time for passengers about 14%.