🎓 Lesson 22
D5
Haulage Optimization Diagnostic Quiz
Haulage optimization is about moving blasted rock from the mine face to the processing plant or waste dump as quickly, safely, and cost-effectively as possible.
🎯 Learning Objectives
- ✓ Calculate haul cycle time including loading, travel, dumping, and return phases
- ✓ Analyze payload utilization and its impact on fleet productivity and tire wear
- ✓ Design an optimal truck-shovel matching ratio using bucket fill factor and cycle time balance
- ✓ Apply queuing theory to evaluate shovel waiting time and identify bottlenecks
- ✓ Explain how road gradient and rolling resistance affect fuel consumption and cycle time
📖 Why This Matters
In open-pit mines, haulage can consume 30–50% of total operating costs—and up to 60% of diesel fuel use. A 5% improvement in haulage efficiency can save millions annually. Poorly optimized haulage leads to underutilized shovels, excessive truck idling, premature tire failure, and missed production targets. This lesson equips you to diagnose and fix real-world haulage inefficiencies—not just run simulations, but make field-validated decisions.
📘 Core Principles
Haulage optimization rests on three interdependent pillars: (1) Equipment Matching—ensuring shovel bucket size, truck payload capacity, and fill factor align to minimize cycle time variance; (2) Network Design—road width, gradient (ideally ≤8%), curvature, and surface quality directly govern safe speed and rolling resistance; (3) Operational Logic—dispatch algorithms (e.g., First-Come-First-Served vs. Load-and-Go) determine queue dynamics and utilization. Critically, optimization is not static: it evolves with bench advancement, ore grade changes, and fleet aging. Real-time telematics data now enables adaptive control—but only if engineers understand the underlying physics and constraints.
📐 Total Haul Cycle Time
Cycle time determines fleet requirement and productivity. It includes loading time (shovel-dependent), loaded travel time, dumping time, and empty return time. Accurate estimation requires accounting for acceleration/deceleration, gear shifts, and speed limits on curves and gradients.
Total Haul Cycle Time (T_cycle)
T_cycle = T_load + T_loaded + T_dump + T_emptyTotal time for one complete haul cycle, used to size fleet and assess productivity.
Variables:
| Symbol | Name | Unit | Description |
|---|---|---|---|
| T_load | Loading time | min | Time for shovel to load truck, including positioning and swing |
| T_loaded | Loaded travel time | min | Time to travel from loading point to dumping point, adjusted for gradient and speed limits |
| T_dump | Dumping time | min | Time to unload, position, and prepare for return |
| T_empty | Empty return time | min | Time to return from dump to loading point, adjusted for gradient and speed |
Typical Ranges:
130–190 t trucks in medium-depth pits: 9 – 14 min
Ultra-class trucks (>360 t) in deep pits (>500 m): 16 – 22 min
💡 Worked Example
Problem: A 190-ton payload CAT 793 truck loads at a hydraulic shovel with 12 m³ bucket (rock density = 2.4 t/m³, fill factor = 0.92). Average loaded speed = 32 km/h; empty speed = 40 km/h. Haul distance = 2.8 km (uphill 6.2% grade); return = 3.1 km (downhill 4.5% grade). Loading time = 32 sec; dumping = 18 sec. Calculate T_cycle in minutes.
1.
Step 1: Compute payload = 12 m³ × 2.4 t/m³ × 0.92 = 26.5 t → well below 190-t rating → no payload constraint.
2.
Step 2: Loaded travel time = (2.8 km ÷ 32 km/h) × 60 = 5.25 min. Apply 15% gradient penalty: 5.25 × 1.15 = 6.04 min.
3.
Step 3: Empty return time = (3.1 km ÷ 40 km/h) × 60 = 4.65 min. Apply 10% downhill benefit: 4.65 × 0.90 = 4.19 min.
4.
Step 4: Sum all phases: loading (32/60 = 0.53 min) + loaded travel (6.04 min) + dumping (18/60 = 0.30 min) + return (4.19 min) = 11.06 min.
5.
Step 5: Round to nearest 0.1 min: T_cycle = 11.1 min — within typical range of 9–14 min for mid-size fleets.
Answer:
The total haul cycle time is 11.1 minutes, which falls within the safe and typical range of 9–14 minutes for 130–190 t trucks in conventional open-pit operations.
🏗️ Real-World Application
At BHP’s Escondida copper mine (Chile), haulage optimization reduced average cycle time by 1.8 minutes per trip after reprofiling a 4.2-km access ramp—reducing gradient from 9.3% to 7.1%, upgrading surfacing to stabilized gravel, and installing variable-speed limit signage. Combined with revised dispatch logic (priority to fully loaded trucks), this increased fleet utilization from 68% to 79% and cut fuel consumption by 11.3 L/ton-km—yielding $4.2M annual savings. Telematics confirmed 92% of trucks now operate within ±0.5 km/h of target speed on critical segments.
🔧 Interactive Calculator
🔧 Open Cycle Time📋 Case Connection
📋 Canadian Gold Mine: Steep Ramp Optimization in Narrow Vein Underground
Excessive truck cycle times and premature tire/brake wear due to suboptimal ramp gradient (15%) combined with tight hori...
📋 South African Platinum Mine: Waste Dump Reclaim Optimization
Inefficient haulage routing and underutilized fleet capacity during waste dump reclamation, resulting in excessive diese...