Multi-Strategy Based Train Re-Scheduling During Railway Traffic Disturbances

Disruptions and delays in railway traffic networks due to different types of disturbances is a frequent problem in many countries. When disruptions occur, the train traffic dispatchers may need to re-schedule the traffic and this is often a very demanding and complicated task. To support the train traffic dispatchers, we propose to use a parallelized multi-strategy based greedy algorithm. This paper presents three different parallelization approaches: (i) Single Strategy with a Partitioned List (i.e. the parallel processes originate from different starting points), (ii) Multiple Strategies with a Non-Partitioned List, and (iii) Multiple Strategies with a Partitioned List. We present an evaluation for a busy part of the Swedish railway network based on performance metrics such as the sum of all train delays at their final destinations and the number of delayed trains. The results show that parallelization helps to improve the solution quality. The parallel approach (iii) that combines all re-scheduling strategies with a partitioned list performs best among the three parallel approaches when minimizing the total final delay. The main conclusion is that the multi-strategy based parallel approach significantly improves the solution for 11 out of 20 disturbance scenarios, as compared to the sequential re-scheduling algorithm. The approach also provides an increased stability since it always delivers a feasible solution in short time