Real-time Rescheduling in Distributed Railway Network: An Agent-Based Approach

TL;DR

This paper presents an agent-based, real-time rescheduling method for large railway networks using Petri-Nets and Markov Decision Processes, effectively reducing train delays after disasters.

Contribution

It introduces a novel distributed constraint optimization approach with autonomous agents for dynamic railway rescheduling.

Findings

Significantly reduces train delays post-disaster.

Validated on Eastern Railways, India with various disaster scenarios.

Outperforms existing rescheduling methods.

Abstract

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 schedule so as 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 times in such a model is handled as Markov Decision Processes (MDP ). For solving the rescheduling problem, a istributed 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 actual schedule of the Eastern Railways, India by constructing vari- ous disaster scenarios using the Java Agent DEvelopment Framework (JADE). When compared to the existing approaches, the proposed framework substantially reduces the delay of trains after rescheduling.