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Blackbook.ai

Constraint Programming Scheduling

A rail transport company engaged Blackbook.ai to streamline and scale their constraint programming project to improve train scheduling.

Client Industry

Transport

Technology stack

Predictive Analysis

Machine Learning

Industry

Transport

Technology stack

Predictive Analysis

Machine Learning

Industry

Transport

Technology stack

Predictive Analysis

Machine Learning

The challenge

Yard controllers for a rail transport company had no way of planning, simulating, optimising or visualising Day Of Operations (DOO) train movements through the yard. 

This was due to its extreme difficulty to do manually, a lack of dedicated staff and static plans were quickly invalidated by DOO changes. Without some form of intelligent decision support that can react to DOO changes, yard coordinators were left to fend for themselves.

The solution

Blackbook was engaged to assist on the internal project that uses constraint programming to create the ‘optimal’ schedule for a train yard including entry, exit and unload as its main KPI points.

We provided Data Science and MLOps consulting to enable the client to streamline and scale the project to other sites quickly. This included scheduling a multi-kilometre coal train down to a segment of track, while taking into account track geometry constraints, operating constraints, vehicle constraints and user inputs for maintenance.

The outcomes

As a result of Blackbook working on the project, the client was able to scale the project out to other sites resulting in insights or expertise gained by controllers to be transferred to colleagues or new staff much more efficiently to improve traffic flow.

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