📋 Case Study
Mine Planning & Scheduling Case Study 1
Industrial application
🏗️ Project Overview
Open-pit copper mine in northern Chile; 120 Mt annual throughput; 25-year mine life; complex geology with variable ore grades and multiple waste rock types.
🎯 Challenge
Inconsistent production scheduling due to inaccurate grade estimation and inflexible short-term plans, leading to 18% monthly ore grade variance, stockpile bottlenecks, and mill underutilization (average 68% capacity utilization).
🔧 Design Approach
Integrated stochastic block model + mixed-integer programming (MIP) optimization for long-term (5-year) and short-term (monthly) scheduling; incorporated geological uncertainty via conditional simulation, dynamic stockpile management constraints, and real-time grade reconciliation feedback loops.
📐 Design Diagram
AI-generated project design illustration
📐 Key Calculations
Optimal Cut-off Grade
COG = (P × G × (1 − R) − C) / (P × (1 − R))
Result: 0.32% Cu
Balanced net present value maximization against processing capacity and recovery losses; reduced low-grade ore dilution by 22%.
Minimum Practical Bench Height for Selective Mining
H_min = (2 × σ_y × tan(φ)) / (γ × FS)
Result: 8.4 m
Ensured geotechnical stability while enabling selective extraction of high-grade zones; decreased dilution from 19% to 11%.
Monthly Production Flexibility Index (MPFI)
MPFI = 1 − (σ_grade / μ_grade)
Result: 0.87
Quantified schedule robustness; increase from 0.62 to 0.87 confirmed improved grade predictability and operational responsiveness.
📊 Results
Metrics: Grade variance reduced from 18% to 5.3%, Mill utilization increased to 92%, NPV uplift: USD 1.4B over life-of-mine, Schedule adherence improved from 71% to 94%
The integrated planning framework enabled adaptive, grade-driven scheduling, eliminated chronic stockpile congestion, and delivered consistent mill feed quality—resulting in sustained productivity gains and significant economic value creation.
💡 Lessons Learned
- •Geological uncertainty must be explicitly modeled—not averaged—in scheduling inputs
- •Real-time grade reconciliation requires seamless integration between mine control, survey, and lab systems
- •Stakeholder alignment across geology, mining, and processing is critical for constraint validation in MIP models
✅ Key Takeaways
- 1Robust mine scheduling requires coupling stochastic resource modeling with deterministic operational optimization—and continuous feedback calibration