A Multi-Objective Decision Support System for Real-Time Train Rescheduling

This study proposes a multi-objective simulation-based optimization framework to effectively manage the train traffic after the occurrences of a disturbance caused by a partial/full blockage. In such conditions, the train orders and the corresponding priorities can be changed to effectively manage the disturbed situation. At this point, a multi-objective version of the variable neighborhood search meta-heuristic is proposed to solve the real-time traffic rescheduling problem and generate Pareto frontiers. The obtained Pareto optimal solutions for disturbance management model supports the decisions made by the rail controllers to find a trade-off between both user and operator viewpoints. We evaluate the proposed approach on a set of disturbance scenarios covering a large part of the Iranian rail network. The computational results demonstrate that the proposed model can generate good-quality disposition timetables with the minimum total average delay of trains at destinations and deviation from the initial timetable. The results indicate that the disturbance management methodology has important advantages in producing practical solution quickly when compared to existing solutions.