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.
