🎓 Lesson 3
D3
Advanced Mine Planning & Scheduling
Mine planning and scheduling is the process of deciding what, where, when, and how much to mine—and in what order—to safely and profitably extract resources over time.
🎯 Learning Objectives
- ✓ Calculate optimal bench height and advance rate for a given orebody geometry and fleet capacity
- ✓ Design a long-term mine schedule using Lerchs-Grossmann (LG) pit limits and nested pit analysis
- ✓ Analyze schedule robustness by applying Monte Carlo simulation to geological and cost uncertainty
- ✓ Apply critical path method (CPM) to identify and mitigate bottlenecks in short-term blast-to-haul sequences
- ✓ Explain trade-offs between NPV maximization, waste stripping ratio, and social license timelines
📖 Why This Matters
Poor mine planning can turn a profitable deposit into a financial liability—even with excellent geology. A 5% error in scheduling can reduce NPV by 15–25% over life-of-mine; conversely, advanced scheduling enabled BHP’s Olympic Dam to extend mine life by 8 years through optimized sequencing. This lesson bridges geology and finance—teaching you how to translate rock mass data into executable, value-driven time plans.
📘 Core Principles
Mine planning operates across three temporal horizons: strategic (10–20 yr), tactical (1–5 yr), and operational (daily–monthly). Strategic planning defines ultimate pit limits using LG algorithms and economic parameters (ore price, costs, discount rate). Tactical planning partitions the ultimate pit into phases (pushbacks) considering infrastructure, access, and blending. Operational scheduling resolves equipment allocation, blast timing, and haulage routing—integrating real-time GPS fleet data and grade control feedback. Modern practice treats uncertainty explicitly: geological risk (grade, continuity) and operational risk (downtime, weather) are modeled probabilistically—not deterministically—to avoid over-optimism and schedule collapse.
📐 Lerchs-Grossmann Pit Limit Value Calculation
The LG algorithm determines the maximum-value set of blocks that can be economically mined, based on block value (revenue minus cost). Each block’s value is calculated individually, then the algorithm finds the largest connected subset with positive cumulative value—subject to slope constraints.
Block Economic Value (BEV)
BEV = (G × R × P) − (C_m + C_p) × TMonetary value of a single mining block, determining its inclusion in the optimal pit.
Variables:
| Symbol | Name | Unit | Description |
|---|---|---|---|
| G | Grade | % metal or g/t | Metal content per unit mass of ore |
| R | Recovery | decimal (0–1) | Fraction of metal recovered during processing |
| P | Metal Price | $/unit mass (e.g., $/lb or $/g) | Market price of the commodity |
| C_m | Mining Cost | $/tonne | Cost to drill, blast, load, and haul the block |
| C_p | Processing Cost | $/tonne | Cost to crush, grind, and recover metal |
| T | Block Tonnage | tonnes | Mass of the block |
Typical Ranges:
Copper porphyry: $−50 to +$120/tonne
Gold open-pit: $−200 to +$350/tonne
💡 Worked Example
Problem: Given: copper price = $3.20/lb, recovery = 88%, processing cost = $18/tonne, mining cost = $3.50/tonne, block tonnage = 10,000 t, grade = 0.62% Cu.
1.
Step 1: Convert grade to lbs Cu/tonne → 0.62% × 2000 lb/tonne = 12.4 lb Cu/tonne
2.
Step 2: Calculate revenue → 10,000 t × 12.4 lb/t × $3.20/lb = $396,800
3.
Step 3: Calculate total cost → 10,000 t × ($18 + $3.50) = $215,000
4.
Step 4: Compute block value → $396,800 − $215,000 = $181,800 (> 0 → economic)
Answer:
The block value is $181,800, confirming inclusion in the LG pit. For a 100-block model, LG would solve ~10⁶ connectivity constraints to find the globally optimal pit shell.
🏗️ Real-World Application
At Newmont’s Boddington Mine (Western Australia), integration of stochastic geological modeling with dynamic scheduling reduced forecast variance in quarterly production by 37%. Using 500 conditional simulations of gold grade and hardness, planners generated risk-weighted pushback schedules—delaying high-risk zones until additional drill data reduced uncertainty. This increased realized grade accuracy from ±22% to ±9% and deferred $420M in unnecessary waste removal—validated by post-blast reconciliation within 1.8% of scheduled tonnes.
🔧 Interactive Calculator
🔧 Open Mine Planning & Scheduling Calculator📋 Case Connection
📋 Mine Planning & Scheduling Case Study 1
Inconsistent production scheduling due to inaccurate grade estimation and inflexible short-term plans, leading to 18% mo...
📋 Mine Planning & Scheduling Case Study 2
Inconsistent haul truck utilization due to static, annual mine plans that failed to account for real-time geotechnical v...