Train schedule optimization in a high-speed railway system using a hybrid simulation and meta-model approach. (December 2019) - Hassannayebi, Erfan; Boroun, Morteza; Alaei Jordehi, Shafagh; Kor, Hamrah
Highlights: This study proposes a meta-model simulation-based optimization (SBO) approach to rail operation planning. The modeling framework is based on a hybrid discrete-event simulation and response surface methodology (RSM). The method considers the second-order analysis of stationary point processes. The validity of the results is confirmed using the real instances of a high-speed rail transit line. Abstract: In urban metro systems, several factors such as passenger congestion may affect the movement of trains, resulting in significant deviations from the planned timetable. This study proposes a meta-model simulation-based optimization approach for minimizing the passenger wait time under random disturbances. The modeling framework is based on a hybrid discrete-event simulation and response surface methodology (RSM), thereby capturing the benefits of both methods. The verification process is handled using the calculation of second-order derivate along with an Analysis of variance (ANOVA) test. Simulation meta-data that is most consistent with the data collected from simulation experiments are used to describe the relationship between response variables, i.e., average waiting time per passenger, and input variable, i.e., time intervals between two departures. The optimal parameters of the RSM are decided using a factorial design. The validity of the results is confirmed using the real instances of a high-speed rail transit line.