Abstract
In recent years, rapid rail transit systems have played a unique role in transportation systems due to the demand increase in accommodating passengers. This study proposes a simulation–optimization method to improve the resiliency of the train timetable in rapid transit rail lines under uncertainty associated with the passenger flow and train running times. The aim is to evaluate the resiliency of the train timetable through a discrete-event simulation (DES) model and to provide an optimized schedule with the maximum degree of resiliency against random disruptions caused by passenger flow fluctuations. The problem is first formulated as a mixed-integer nonlinear programming model. The validity of the DES model is justified using convergence test analysis of the response variable, i.e., average passenger wait time, during the simulation run. Due to the complexity of the problem, a variable neighborhood search (VNS) and a genetic algorithm (GA) are proposed to solve large instances of the problem. A self-adaptive tuning approach is proposed to adjust the GA parameters. The benefit of the simulation–optimization approach is verified through numerical experiments based on real cases adopted from Line No. 1 of the Tehran underground metro system. The results indicate that the simulation-based optimization method could improve the resiliency of train services by almost 16.7%, on average, as against the all-stop service operation. The average improvement of using VNS as against the GA is about 47%. Also, VNS method provides better-quality solutions by average optimality gap of about 14% in all test instances when compared to an exact solution method, i.e., branch-and-reduce algorithm.
Published Version
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