Recovering the timetable after a delay is essential to the smooth and efficient operation
of the railways for both passengers and railway operators. Most current
railway rescheduling research concentrates on static problems where all delays are
known about in advance. However, due to the unpredictable nature of the railway
system, it is possible that further unforeseen incidents could occur while the trains
are running to the new rescheduled timetable. This will change the problem, making
it a dynamic problem that changes over time. The aim of this work is to investigate
the application of ant colony optimisation (ACO) to dynamic and dynamic multiobjective
railway rescheduling problems. ACO is a promising approach for dynamic
combinatorial optimisation problems as its inbuilt mechanisms allow it to adapt to
the new environment while retaining potentially useful information from the previous
environment. In addition, ACO is able to handle multi-objective problems by
the addition of multiple colonies and/or multiple pheromone and heuristic matrices.
The contributions of this work are the development of a junction simulator to
model unique dynamic and multi-objective railway rescheduling problems and an
investigation into the application of ACO algorithms to solve those problems. A
further contribution is the development of a unique two-colony ACO framework to
solve the separate problems of platform reallocation and train resequencing at a UK
railway station in dynamic delay scenarios.
Results showed that ACO can be e
ectively applied to the rescheduling of trains
in both dynamic and dynamic multi-objective rescheduling problems. In the dynamic
junction rescheduling problem ACO outperformed First Come First Served
(FCFS), while in the dynamic multi-objective rescheduling problem ACO outperformed
FCFS and Non-dominated Sorting Genetic Algorithm II (NSGA-II), a stateof-
the-art multi-objective algorithm. When considering platform reallocation and
rescheduling in dynamic environments, ACO outperformed Variable Neighbourhood
Search (VNS), Tabu Search (TS) and running with no rescheduling algorithm. These
results suggest that ACO shows promise for the rescheduling of trains in both dynamic
and dynamic multi-objective environments.Engineering and Physical Sciences Research Council (EPSRC
