Train Unit Shunting and Servicing: a Real-Life Application of Multi-Agent Path Finding

TL;DR

This paper explores applying multi-agent path finding techniques to optimize train shunting and servicing operations, aiming to improve yard capacity utilization and maintenance scheduling through a complete algorithm for relaxed problem models.

Contribution

It introduces a novel extension of multi-agent path finding models to address the complex train shunting and servicing problem, providing a basis for bounds and capacity planning.

Findings

Potential of multi-agent path finding for train yard optimization

Development of a complete algorithm for relaxed problem models

Insights into capacity planning and heuristic evaluation

Abstract

In between transportation services, trains are parked and maintained at shunting yards. The conflict-free routing of trains to and on these yards and the scheduling of service and maintenance tasks is known as the train unit shunting and service problem. Efficient use of the capacity of these yards is becoming increasingly important, because of increasing numbers of trains without proportional extensions of the yards. Efficiently scheduling maintenance activities is extremely challenging: currently only heuristics succeed in finding solutions to the integrated problem at all. Bounds are needed to determine the quality of these heuristics, and also to support investment decisions on increasing the yard capacity. For this, a complete algorithm for a possibly relaxed problem model is required. We analyze the potential of extending the model for multi-agent path finding to be used for such a relaxation.