An Integrated Optimization Method of High-Speed Railway Rescheduling Problem at the Network Level

Abstract

For high-speed railway operations at the network level, unforeseen events that lead to operation interruptions are inevitable, which should be handled within a short period of time to reduce the influence of the events as much as possible. This paper introduces an integrated optimization method to deal with rescheduling problems at the railway network level under emergencies, rescheduling the train timetable, and utilizing the train sets. train set A three-objective optimization model is proposed with the aim of minimizing additional operation costs, total delay, and the number of transfer passengers. Then, an algorithm based on NSGA-III is proposed to solve the model. Computational experiments on real data are conducted to show the adaptability of the model and algorithm. The average optimization rate of the three objectives is 12.12%, 14.12%, and 10.57%, indicating the effectiveness of the method. Moreover, more experiments on a railway network in China are being conducted to analyze which section and which time have the greatest impact on the railway network when emergencies occur. According to the experiment, the bottleneck section is section 15, and the bottleneck time is 11:00 am. In addition, the importance of all the depots is discussed, and depot II is selected as the most important depot.

Stability assessment of railway trackwork scheduling in Sweden

“…Over the past decades, the challenge of train rescheduling has become a crucial concern for railway management, capturing the interest of researchers globally and resulting in a variety of solutions [53]. The employed methods aimed at minimising train disruptions, train delays, travel demand and passenger inconvenience, and operational costs, while also enhancing energy efficiency [4,10,11,36,46,48,57]. Notable contributions include the development of models that not only aim to optimise timetables during disruptions but also take into account the dynamic nature of passenger demand and system resilience.…”

“…Notable contributions include the development of models that not only aim to optimise timetables during disruptions but also take into account the dynamic nature of passenger demand and system resilience. For example, [4,10,23,46,52] [11,23,49,57] focus on network-level optimisation of train schedules under emergencies and enhancing system resilience. Reynolds et al and Xiu et al [36,48] provide insights into the fairness of optimisation models for train rescheduling and strategies to handle rescheduling uncertainties.…”