ToolFusion
📋 Case Study

Limestone Mine ROC for Autonomous Haulage Fleet Coordination

Fleet coordination conflicts during simultaneous loading/unloading causing queue buildup and fuel waste

🏗️ Project Overview

Martin Marietta’s Texas limestone quarry with 22 autonomous haul trucks

🎯 Challenge

Fleet coordination conflicts during simultaneous loading/unloading causing queue buildup and fuel waste

🔧 Design Approach

Centralized fleet orchestration engine with dynamic priority scheduling, geofenced conflict zones, and real-time payload telemetry integration

📐 Design Diagram

Limestone Mine ROC: Autonomous Haulage Fleet Coordination Conflict Zone Simultaneous loading/unloading → queue buildup & fuel waste ROC Engine Dynamic Priority Scheduler Geofence A Geofence B Real-Time Payload Telemetry → g/t, payload mass, cycle timestamp σ² reduction: 63% Cycle time variance Fuel saved: 11.4 g/t per tonne transported

AI-generated project design illustration

📐 Key Calculations

Cycle Time Variance Reduction

σ²_pre − σ²_post
Result: 63% lower variance
Stabilized production throughput

Fuel Efficiency Gain

(Pre-ROC g/t) − (Post-ROC g/t)
Result: 11.4 g/t saved
Directly tied to idle time reduction

📊 Results

Truck utilization ↑ 22%, average payload accuracy ↑ 98.7%, unplanned stops ↓ 76%, ROI achieved in 14 months

💡 Lessons Learned

  • •Autonomous fleet ROC requires deterministic scheduling—not just telemetry dashboards
  • •Payload sensor calibration traceability is critical for dispatch logic integrity

✅ Key Takeaways

  • 1Autonomous fleet ROC requires deterministic scheduling—not just telemetry dashboards
  • 2Payload sensor calibration traceability is critical for dispatch logic integrity