Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study
This is an incremental approach for railway operations to improve scalability in real-time rescheduling.
The paper tackles the railway rescheduling problem by proposing a core problem that restricts scope to only affected trains in time and space, and reports preliminary results using the Flatland simulation environment to highlight potential and challenges.
With the aim to stimulate future research, we describe an exploratory study of a railway rescheduling problem. A widely used approach in practice and state of the art is to decompose these complex problems by geographical scope. Instead, we propose defining a core problem that restricts a rescheduling problem in response to a disturbance to only trains that need to be rescheduled, hence restricting the scope in both time and space. In this context, the difficulty resides in defining a scoper that can predict a subset of train services that will be affected by a given disturbance. We report preliminary results using the Flatland simulation environment that highlights the potential and challenges of this idea. We provide an extensible playground open-source implementation based on the Flatland railway environment and Answer-Set Programming.