Simulation-optimization framework for train rescheduling in rapid rail transit

One of the primary challenges of re-planning in high-speed urban railways is the randomness of disruptive events. In this study, an integrated disturbance recovery model presented in which short-turn and stop-skip service operations are optimized together to minimize the average of passengers' waiting times. This study develops a discrete-event simulation model that employs a variable neighborhood search algorithm to maintain the service level under infrastructure elements' unavailability. Due to the unpredictable nature of the incidents, the uncertainty associated with obstruction duration is experimentally analyzed through probabilistic scenarios. The computational experiments are conducted on some test cases of the Tehran Metropolitan Network, and the benefits of the combined control strategy are justified. The outcomes validate the superior performance of the proposed simulation-optimization method over existing state-of-the-art methods. The optimal solutions provide urban rail companies with robust decision options where the maximum recoverability resulting from rescheduled services are expected. The integrated control policy result can also support the analysis of secondary train delay and timetable deviations. The computational results afford practical insights by showing the strong potential to improve the system's responsiveness by minimizing the random disturbances' cascading effects.