MiningMath

Across mining projects and operations, mine planning teams are under constant pressure. Engineers must balance economic value, operational constraints, and an environment of growing uncertainty. Even so, many projects still rely on sequential methods that separate pit optimization and pushback design from production scheduling.

In practice, this separation creates a recurring problem. After defining nested pits and pushbacks, the plan often conflicts with real operating limits and the project fails to reach its highest potential. Engineers then rely on additional criteria and supporting algorithms to make the plan workable. This process often reduces the Net Present Value of the project and may still fail to meet all required constraints over the full life of mine.

The root cause lies in traditional planning logic. Methods based on Lerchs Grossmann or Pseudoflow work in isolated steps, defining the economic envelope first, adjusting pushbacks next, and only then attempting to fit a production schedule. This order ignores a basic reality. Operational constraints should shape the pit from the start, not be added later as fixes.

MiningMath introduces a different technical approach to this problem through an integrated mine optimization engine. The company developed a methodology known as Single-step Mine Optimization. This approach integrates pit geometry, block destinations, and production schedules into a single mathematical formulation. Instead of solving the problem in parts, all relevant variables are addressed at the same time.

This integration changes how planning decisions are made. The pit is no longer a purely economic outcome that must later be adapted to operational limits. It reflects real operating conditions from the beginning. Plant capacity, blending targets, and physical access constraints are embedded directly in the optimization process.

A key distinction of this methodology is how geometric constraints are treated and how this directly affects equipment maneuverability, access design, and operational feasibility. Parameters such as minimum mining width, minimum bottom width, and vertical advance rate are no longer rules applied after optimization. They become formal constraints within the model. As a result, the algorithm does not generate impractical shapes simply to inflate theoretical value. It searches for the best economic outcome within the physical limits defined by the operation.

The impact of this approach can be observed in applied studies. In a comparative analysis conducted on the La Cocota copper deposit in northern Chile, single-step optimization delivered measurable gains when compared to a traditional planning workflow. Net Present Value increased by approximately eighteen percent under the same plant assumptions. Waste movement was reduced by nearly one hundred million tonnes. Positive cash flow began two years earlier, significantly improving project returns.

These results highlight an important issue in sequential planning processes. A portion of asset value often remains hidden. It is masked by intermediate decisions, fixed cut-off assumptions, and subjective manual adjustments. By integrating sequencing and block destinations from the outset, optimization captures this value more consistently and transparently.

Adopting a new planning methodology often prompts questions among experienced technical teams, particularly regarding compatibility with existing tools. MiningMath does not aim to replace mine design software already in use. In this workflow, mining phases are a direct consequence of the optimization results generated in MiningMath. Once these phases are defined and designed, engineers remain free to refine production schedules either within MiningMath itself or by using any mine scheduling package of their preference.

Another relevant capability is the generation and evaluation of multiple operational scenarios. Instead of relying on a single base case, the methodology supports decision trees and multivariable sensitivity analyses. Engineers can test variations in metal prices, recovery assumptions, environmental constraints, or operational limits. The effects of each assumption are reflected directly in economic indicators such as Net Present Value and cash flow timing.
This capability expands the role of the planning engineer within the project. The focus shifts from delivering a fixed production schedule to supporting strategic decision making. Each hypothesis can be tested, compared, and discussed using consistent data. This reduces the risk of late surprises and improves confidence in capital and operating decisions.

Current conditions in the mining industry reinforce the importance of this shift. Market volatility, stricter environmental requirements, and operational disruptions have made single-scenario plans increasingly fragile. Effective planning is no longer about finding one perfect answer. It is about understanding limits, alternatives, and real trade-offs.

In this context, single-step optimization goes beyond a change in software. It reflects an evolution in mine planning governance. Subjective corrections play a smaller role. Economic value and operational feasibility are considered together from the start. For mining companies seeking to protect asset value, this shift supports more resilient, transparent, and informed decision making.