Multi-agent-based dynamic railway scheduling and optimization: a coloured petri-net model

This paper addresses the issues concerning the rescheduling of a static timetable in case of a disaster, encountered in a large and complex railway network system. The proposed approach tries to modify the existing schedule to minimise the overall delay of trains. This is achieved by representing the rescheduling problem in the form of a Petri-Net and the highly uncertain disaster recovery time in such a model is handled as Markov decision processes (MDP). For solving the rescheduling problem, a distributed constraint optimisation (DCOP)-based strategy involving the use of autonomous agents is used to generate the desired schedule. The proposed approach is evaluated on the real-time data set taken from the Eastern Railways, India by constructing various disaster scenarios using the Java Agent DEvelopment Framework (JADE). The proposed framework, when compared to the existing approaches, substantially reduces the delay of trains after rescheduling.