Minimising the ongoing impact of train delays has benefits to both the users of the railway system and the railway stakeholders. However, the efficient rescheduling of trains after a perturbation is a complex real-world problem. The complexity is compounded by the fact that the problem may be both dynamic and multi-objective. The aim of this research is to investigate the ability of ant colony optimisation algorithms to solve a simulated dynamic multi-objective railway rescheduling problem and, in the process, to attempt to identify the features of the algorithms that enable them to cope with a multi-objective problem that is also dynamic. Results showed that, when the changes in the problem are large and frequent, retaining the archive of non-dominated solution between changes and updating the pheromones to reflect the new environment play an important role in enabling the algorithms to perform well on this dynamic multi-objective railway rescheduling problem.

Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it Communicated by D. Neagu.

Efficient rescheduling after a perturbation is an important concern of the railway industry. Extreme delays can result in large fines for the train company as well as dissatisfied customers. The problem is exacerbated by the fact that it is a dynamic one; more timetabled trains may be arriving as the perturbed trains are waiting to be rescheduled. The new trains may have different priorities to the existing trains and thus the rescheduling problem is a dynamic one that changes over time. The aim of this research is to apply a population-based ant colony optimisation algorithm to address this dynamic railway junction rescheduling problem using a simulator modelled on a real-world junction in the UK railway network. The results are promising: the algorithm performs well, particularly when the dynamic changes are of a high magnitude and frequency.

Abstract-Train delays at stations are a common occurrence in complex, busy railway networks. A delayed train will miss its scheduled time slot on the platform and may have to be reallocated to a new platform to allow it to continue its journey. The problem is a dynamic one because while reallocating a delayed train further unanticipated train delays may occur, changing the nature of the problem over time. Our aim in this study is to apply ant colony optimisation (ACO) to a dynamic platform reallocation problem (DPRP) using a model created from real-world train schedule data. To ensure that trains are not unnecessarily reallocated to new platforms we introduce a novel best-ant-replacement scheme that takes into account not only the objective value but also the physical distance between the original and the new platforms. Results showed that the ACO algorithm outperformed a heuristic that places the delayed train in the first available time-slot and that this improvement was more apparent with high-frequency dynamic changes.