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1. www.minemax.com - Perth, Australia - Denver, USA
www.minemax.com - Perth, Australia - Denver, USA
Practical Applications of Minemax Scheduler for Strategic
Mine Planning
Joe Kraft – Minemax

2. OFFICES IN PERTH, AUS
AND DENVER, USA.
EXPERTS IN STRATEGIC
SCHEDULE OPTIMISATION
GLOBAL SOFTWARE
IMPLEMENTATIONS OVER
25 YR HISTORY
MEMBER OF THE
CONSTELLATION
SOFTWARE FAMILY
More than a mining software company
We’re your go-to mine planning partners.

3. Minemax Scheduler Professional
Simultaneously
• Conquer strategic complexity
• Solve for cut-off and destinations
• Leverage stockpiles, blending, products
• Integrate and solve for CAPEX
• Optimise schedule for max NPV

4. Standard Practice – Strategic Mine Scheduling
Mine Planners are solving larger and more complex mine scheduling problems
everyday.
– Considering:
▪ Time value of money and project value
▪ Sequencing fundamentals driven by economic model
▪ Blending, destination, and/or product grade restrictions
▪ Constraints to revenue generation
▪ Multi-period solving

5. The challenges continue…
What if there are multiple downstream competing options, complexities, or
‘beyond-the-pit’ requirements?
– Such as:
▪ Incorporating pit backfill
▪ Planning for constrained haulage routes
▪ Conditional waste delivery for tailings dams
▪ Assessing capital investment for expansion elements
▪ Directly incorporating corporate financial objectives
How do we leverage these situations in mine schedule optimisation?

6. Pit Backfill

7. Pit Backfill – Modelling aspects
• Looking to solve for waste destinations at the same time as the schedule
• Sequencing-dependent location interaction
• Precedence controls between pits/backfills
• Haulage distance/time differential benefit is likely the incentive
Pit Backfill Modelling

8. Waste cycle decomposition
from pit/phase bench to
surface dump
Waste cycle decomposition from
pit/phase bench to backfill dump
Loaded Direction
CPP
Loaded Direction
CPP
BF
Mechanics of the cost/benefit: Haulage
• When solving for destinations – need to be accurate with parameters
– Modelling truck hour burden to enable decision-making during solve
– Cycle time network decomposed to apply explicitly to routes (use of CPP)
Pit Backfill Modelling

9. Planning for Constrained Haulage Routes

10. Context of the problem
• Seeking lowest cost, most automated scheduling software tends to send all material
on shortest route
• All trucks on shortest route may not always be practical or achievable
• As a result, the schedule would be infeasible (under-calling truck requirements
and/or ability to deliver plan tonnage)
B A
Constrained Route Modelling

11. Route Constraints – Modelling Aspects
• Modelling routes as processes (decisions), units of material can only take one path
• Route availability/capacity modelled as discretised constraint
• Optimisation solves for what material on what route
• Combined with haulage fleet capacity constraint (operating in parallel)
Constrained Route Modelling

12. Conditional Waste Delivery for Tailings Dams

13. Modelling tailings dam material delivery
• Certain spec material required over time
• Seeking to avoid/reduce stockpiling and re-handling
• Integrate decisions in optimisation model where they can influence mine sequence
Conditional Waste Planning

14. Conditional Waste Delivery : Modelling Aspects
• Revenue from mill is dependent on tailings storage availability
• Volume for tailings dam modelled with specific design capacity
• Design capacity must be accumulated by certain timeline or mill shuts down
• Haulage (truck hours) differential for each waste destination.
Conditional Waste Planning

15. Modelling Capex within the Schedule Optimisation

16. Which constraints are binding the mine schedule?
• Likely to vary from period to period, particularly in blending situations
• Certain constraints, such as the bottleneck(s) to revenue generation, should be binding,
and tied to heavy capital
• Solve for these capex options to evaluate effect on sequence and project value
Spare
Capacity
Period X
RESERVES
Excav
Haul
A
B
D
MARKET
Access
C
Processing/Downstream
Mining
CAPEX Optimization

17. Truck Capex Optimisation: Modelling Aspects
• Truck hours modelled as a process for each destination
• Fleet truck hours expressed as a constraint
• Capex definition for a single truck instance
– Purchase price
– Capacity Release
– Lifespan of Asset
• Only purchases if expanding truck hours (by adding a truck and incurring the cost) will
return greater value for the schedule
CAPEX Optimization

18. Modelling Corporate Financial Objectives

19. Modelling Corporate Financial Objectives
• Seeking to achieve maximum NPV schedule, whilst adhering to company financial
strategy and obligations.
– Cash reserve thresholds
– Debt re-payment schedules
– Capital spending limits
– Minimum cashflow/returns per period
• Minemax Scheduler treats these as financial constraints
Financial Constraints

20. Thank you!