Discrete Optimization on Train Rescheduling on Single-Track Railway: Clustering Hierarchy and Heuristic Search

Abstract

This paper focuses on discrete dynamic optimization on train rescheduling on single-track railway with the consideration of train punctuality and station satisfaction degree. A discrete dynamic system is firstly described to mimic train rescheduling, and a state transition function is specially designed according to the train departure event. The purpose of this function is to improve simulation efficiency by directly confirming the next discrete time. After the construction and analysis of optimization models to discrete dynamic system, a two-stage heuristic search strategy is developed, by using clustering hierarchy theory and stochastic search strategy, to obtain train departure time and arrival time before each state transition of the system. Finally, a numerical experiment is conducted to verify the proposed system, models, and the heuristic search strategy. The result shows that the discrete dynamic system, together with the state transition function and heuristic search strategy, shows better performance in simulation efficiency and solution quality.

Optimized Train Dispatching and Rescheduling During a Disruption in a Bottleneck Section

“…Furthermore, Josyula et al [15] presented a parallel algorithm to efficiently solve the real-time railway rescheduling problem on a multi-core parallel architecture. With the aim of improving simulation efficiency by directly confirming the next discrete time, Zhang et al [16] focused on discrete dynamic optimization on train rescheduling on single-track railway with the consideration of train punctuality and station satisfaction degree. Takagi et al [17] also proposed the use of a Genetic Algorithm (GA) with Object-Oriented Multi-Train Simulator (OOMTS) developed by Birmingham University, as an embedded simulator to optimize the order of route setting.…”