Balancing Competing Goals Through Algorithms

A case study of successful industry and university engagement

Summary

  • Aurizon operates and manages approximately 2,670km of heavy haul rail infrastructure across Queensland.
  • Preventive track maintenance is essential to sustaining an efficient and reliable coal export supply chain as it ensures maximum operational performance and reduces the probability of failure of infrastructure components.
  • Temporary reduction in system throughput capacity can be substantially reduced by optimally aligning preventive maintenance tasks.
  • The University of Newcastle Centre for Optimal Planning and Operations (C-OPT) developed an optimisation-based approach to effectively and efficiently analyse the impact on system-wide throughput capacity of a preventive maintenance schedule (or a possession regime).
  • C-OPT's analysis tool allows Aurizon to develop preventive maintenance schedules that maximise system throughput while ensuring that maintenance requirements are met.

Professor Martin Savelsbergh and Professor Natashia BolandPreventive Maintenance Scheduling in the Rail Industry

Aurizon, previously known as QR National, has over 147 years of rail freight industry experience. With rail services stretching from Cairns in Queensland to Perth in Western Australia, Aurizon moves coal, iron ore and other minerals as well as agricultural and general freight.

The company operates and manages the Central Queensland Coal Network made up of approximately 2,670km of heavy haul rail infrastructure.

The goal of maximising the tonnage transported across a rail network coupled with the need to maintain that network to ensure its operating efficiency is a daunting task. 

Quick assessment and evaluation of candidate track possession regimes greatly enhances the ability to construct track possession regimes that balance these conflicting and competing goals and needs.

Image: C-OPT researchers Professor Martin Savelsbergh and Professor Natasha Boland

A candidate possession regime specifies a list of maintenance tasks, with information for each task including:

  • start date and time
  • end date and time
  • which infrastructure component it affects and by how much. 

To assist in scheduling maintenance tasks while maintaining network efficiency, the Centre for Optimal Planning and Operations (C-OPT) developed a flow-based analysis tool that takes a candidate possession regime and efficiently determines the maximum amount of coal that can be transported from mine load points to coal terminals at the port over a given planning horizon.

CoalThe transport of coal over a rail infrastructure causes deterioration or wear. Maintenance is undertaken to restore the condition of the infrastructure. The coal transports from load points to terminals determined by the optimal flows together with the scheduled maintenance tasks allows the calculation of a wear-to-repair ratio for each of the track sections over a period of time. C-OPT's analysis tool automatically establishes wear-and-repair ratios for all the track sections in additional to determining the maximum throughput.

C-OPT's flow-based analysis tool has become an integral part of the possession regime construction process and not only ensures that Aurizon can meet its contracted coal transport obligations, but that the company also has the ability to increase contracted coal transport volume without having to invest in additional infrastructure by using existing infrastructure more effectively.

C-OPT continues to add features to the analysis tool so as to facilitate the construction of optimal preventive maintenance schedules across the multiple rail networks operated by Aurizon.

Further Information

For further information regarding Optimisation and the flow-based analysis tool developed for Aurizon, please visit the University of Newcastle Centre for Optimal Planning and Operations

For further information regarding collaborative engagement with the University of Newcastle and its researchers, please contact the Research Development Team.