Case study 

Real-time rail scheduling

When a major mining company needed to increase the capacity and reliability of its rail operations, we developed an end-to-end train schedule optimization system that used real-time data to schedule train movements on a complex rail network, updated the schedule as disruptions occurred in the network, added trains whenever it saw the opportunity and optimized utilization of facilities at mines and ports.


days between generated notifications for additional trains

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BRC spends millions of dollars annually for capital expenditure decisions to reduce costs, increase capacity and improve efficiency.

Business problem

Trains transport iron ore over a rail network from mines to ports before being exported. The rail network presented a bottleneck for a major iron ore mining company in Western Australia, with mines spread across 100 square kilometers. Historically, train scheduling had been done manually, resulting in suboptimal schedules and underutilization of tracks and facilities at mines and ports. The company was unable to find a computerized solution for modeling and scheduling its 60 daily trains running in multiple directions on a 300 km-long network of single, double and side tracks, with periodic maintenance events and track outages. When the mining company wanted to increase its rail network’s efficiency and capacity, it hired Optym to develop an optimization-based train scheduling system, RailMax, with the goal of achieving decision automation for its rail operations.

Our approach

Our team worked closely with the company’s rail business unit to learn user expectations and gather business requirements. Then, we developed and deployed the train scheduling system in multiple phases. The first phase of the system went live within one year, and we are continuing to add new capabilities every three and six months since then, and each capability bringing additional value. At the heart of the train scheduling system was a simulation-guided optimization algorithm that simulated one week of train movements within one minute of computational time. It identified numerous scheduling alternatives, evaluated these with respect to a user-defined objective function and recommended the best alternative.
Our end-to-end optimization algorithm considered production targets (demand), the mine plan (supply) and resource constraints, such as railcars and end-facilities, to generate complete train departure schedules. We packaged our algorithm into an interactive decision support system with a graphical user interface that was easy to learn and use. Multiple users with different privileges were able to work in parallel, and each user could create multiple scenarios. We displayed the train schedule using tables, charts and an electronic train graph (ETG), which presented schedules in an intuitive way. Using the ETG, users could add train dwell time, maintenance events, speed restrictions, switch clamps and crew changeover.

Key benefits

  1. Our innovative algorithms integrated the company’s data sources to create accurate and executable train schedules that showed the potential for adding trains with new revenue of several hundred million dollars annually. The project team won an award from company executives for being one of the most value-adding initiatives of 2017.
  2. Train schedules generated by RailMax had lower cycle times, leading to more efficient use of rail infrastructure and increased throughput of the rail network.
  3. The system enabled smart train scheduling on the day of operation and up to a week in advance, eliminating the need for manually-generated short-term schedules. Network coordinators quickly managed deviations by re-optimizing schedules after changes occurred in the rail network.


RailMax has been in production as a business-critical system for the mining company since 2015. We add new capabilities periodically, such as the ability to work with communication-based train control signaling systems. RailMax identified opportunities for additional trains while considering all constraints. The mining company highly valued the system’s intuitive user interface and fast optimization capabilities, as well as the technical support offered by our team.

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