The document discusses advanced methods for next generation mine planning, focusing on optimizing excavation sequences and material blending to meet tonnage, quality, cost, and NPV targets. It highlights the complexities introduced by various factors including material types, business rules, and decision-making processes, and suggests using non-linear optimization techniques such as genetic algorithms and simulated annealing. The proposed solution includes a virtual simulation to model excavation and blending processes, allowing for adaptive decision-making and scenario comparison to enhance business outcomes.

1. Next Generation Mine Planning:
Advanced Scientific Approach to
Optimize your Extraction Sequence
Daniel Spitty
February 2014
SME Conference, Salt Lake City, USA

2. The problem
What is the best increment excavation sequence
and the best material blending combination
such that tonnage, quality, cost, and NPV targets
are met?

3. The challenge
Material complexity
Over 1,000,000 blocks of
material within10,000
increments containing multiple
material types in each.
Business rule complexity
30 year planning horizon broken
up into quarterly buckets, with
the ability to configure 100’s of
business rules differently for
each bucket.
Decision making complexity
Business problems requiring
non-linear approaches to
providing realistic, optimal and
most of all, executable business
outcomes.
Integrated Supply chain complexity
From excavation to haulage to blending
to material destination, the best plan
may be different if the business priority
is tonnage, cost or quality.
Within minutes….
= Material complexity * Supply chain complexity * Business rule complexity * Decision making complexity

4. Non-Linear Problems and Solutions
• Genetic Algorithms
• Simulated Annealing
• Hybrid Neural Networks
• Evolutionary Strategies
• Hill-Climbers
• Ant Systems
• Tabu Search
• Evolutionary Programming
Non-linear optimisation techniques are required to solve
non-linear models:

5. Mine Planning Inputs
●Geospatial block model of the material within the mine
●Haulage road network nodes
●Stockpile and waste dump locations
●Fixed and mobile equipment configuration
●User defined
● KPI targets (desired)
● area aggregations, dependencies,
● business rules,
● material flow
● optimization objective weightings

6. Solution Approach
●2 Key Components
● Excavation – Diggers/Shovels
● excavation sequence determination via intelligent grade control
adaptation of excavated material
● Blending – Haulage/Destination
● product target optimisation via blending
●Uses a virtual simulation-based “world” for entities to function in
●Diggers act as agents in the virtual world, each responsible for
determining their own independent actions
●Maintains a central inventory of available ore that influences the high-
level direction of the agents

7. Solution Approach
●Agents perform adaptive search to determine their best decision
● uses different evaluation heuristics depending on the state of the inventory
● the size of the search is adapted depending on the state of the inventory
●Uses look-ahead to determine future positions in order to assess the
impacts of possible decisions

8. 1. Where does the digger move next?
2. How much does a digger excavate?
3. Is material waste or ore?
4. Is back filling now possible?
5. Which waste dumps do we send the waste to?
6. Is the ore to be sent to crusher or to a stockpile?
7. Which crusher to send to?
8. Which stockpile to send to?
9. What material to draw from which stockpile?
10. How much haulage is needed from pit to crusher?
11. How much haulage is needed from pit to stockpile?
12. How much haulage is needed from stockpile to crusher?
13. Which plant to use?
12
1
8
3
7
2
9
5
10
4
11
6
13
High level decision points

9. Results
●Multiple scenarios can be run and compared to support decisions
● Block extraction sequence
● Mobile equipment assignments and location
● Mobile and fixed equipment utilization
● KPI planned performance

10. Benefits and Conclusion
● Exploring more possibilities
through science techniques
● Science adapting to current and
future projected states to change
decisions
● Enabling informed decision making
through the generation of multiple
scenarios in a timely manner
● Matching the optimization objectives
with the business operating model
through dynamic configuration

11. Thank you!