AI USE CASE
Mine Fleet Dispatch Reinforcement Learning
Dynamically assigns haul trucks to shovels and dump points to maximize mine throughput.
What it is
Reinforcement learning agents continuously re-optimize truck assignments across shovels and dump points, reacting in real time to equipment availability, road conditions, and queue lengths. The system learns from live telemetry and improves over weeks of operation, outperforming rule-based dispatchers on complex multi-shovel layouts. ROI is typically realized within one to two quarters through fuel savings and production uplift.
Data you need
Real-time GPS telemetry and status data from haul trucks, shovel production rates, dump point availability, and historical cycle-time records.
Required systems
- erp
- data warehouse
Why it works
- High-frequency, reliable GPS and equipment-status telemetry across the entire active fleet.
- A dedicated simulation environment (digital twin) used to pre-train the RL agent before live deployment.
- Strong change management to build operator trust and reduce manual overrides during the learning phase.
- Continuous monitoring with a human-in-the-loop escalation path for edge cases and equipment failures.
How this goes wrong
- Sparse or low-quality telemetry data prevents the RL agent from learning a reliable dispatch policy.
- Integration with legacy fleet management systems proves too slow or unreliable for real-time decision-making.
- Operators override the system frequently, breaking the feedback loop needed for continued learning.
- Model trained in one pit layout fails to generalize after mine expansion or redesign.
When NOT to do this
Do not deploy this if the mine operates fewer than 10 active haul trucks or has a simple single-shovel layout, the complexity overhead far outweighs the marginal gain over rule-based dispatch.
Example vendors
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