Abstract
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 has been shown that if the ant solutions in memory become irreparably infeasible it is possible to replace them with elite immigrants, based on the best-so-far ant, and still obtain a good performance.
Highlights
The problem of rescheduling trains after a delay is an important concern of the railway industry
This paper investigates an even more challenging delay situation where there is not just one but multiple unrelated delays occurring over the time period of the investigation
Three of the algorithms were based on the P-ant colony optimization (ACO) algorithm but with the introduction of immigrants to replace the memory after a change
Summary
The problem of rescheduling trains after a delay is an important concern of the railway industry. The problem is further complicated by the fact that, while a train controller is trying to minimise the delay at a particular point in time, more trains will be arriving at the affected area These trains may have different priorities to those already waiting to be rescheduled, which makes the problem a dynamic one that changes over time. Allowing the ants to sequence trains at the stations as well as at the junction means that the ants must have the power to change the arrival order of the trains at the junction This has implications for the memory repair operation that is needed to reinitialise population-based ACO (P-ACO) algorithms after a dynamic change has taken place
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