A heuristic for real-time crew rescheduling during small disruptions

Due to unforeseen problems, disruptions occur at railway passenger operators. Proper real-time crew management is needed to prevent disruptions to spread over space and time. Netherlands railways (NS) has algorithmic support from a solver to obtain good crew rescheduling solutions during big disruptions. However, small disruptions are still manually solved by human dispatchers who have limited solving capacity. In this paper the rescheduling for crews during small disruptions is modeled as an iterative-deepening depth-first search in a tree, which is combined with several OR techniques, obtaining a heuristic method. The heuristic focuses on real-life usability and uses the updated rolling-stock schedule as input. Testing the heuristic on about 5,000 test instances shows that the heuristic delivers good and desirable rescheduling solutions within fraction of seconds, outperforming other well-known methods from the literature.