Mine Plan Robustness Index Using Simulated Work Package Float
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Solution Overview
Problem
Current mine planning methods lack a tool to assess the robustness of mine plans, specifically in terms of their ability to withstand variability in inputs over the plan's timeframe, leading to uncertainty in achieving production targets.
Innovation Solution
A computer-implemented method and system that replicates a mine plan, groups tasks into work packages, applies variance to these packages using simulations (such as Monte Carlo simulations), generates a float representing the duration between work package completions and start-ups, and consolidates this data into a robustness index for display to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If deterministic single-point assumptions are used in mine planning, then the planning process is simple and straightforward, but the plan cannot account for variability and uncertainty in inputs
Solution Approach 1:
The patent transforms the static deterministic mine plan into a dynamic stochastic model that incorporates variability. Instead of using fixed single-point estimates for inputs like ore grade, tonnage, and equipment performance, the system applies probability distributions that allow these parameters to vary within realistic ranges. This dynamic approach enables the plan to adapt to uncertainty while maintaining computational tractability through Monte Carlo simulation.
Solution Approach 2:
The patent changes the nature of input parameters from deterministic single values to stochastic distributions. Each key input parameter (ore grade, tonnage, equipment rates, etc.) is represented by a probability distribution with defined mean, standard deviation, and bounds. This parameter transformation allows the system to capture the inherent variability in mining operations while producing a robustness index that quantifies plan reliability.
2Reliability
If variance is applied to key inputs through simulation, then the robustness and reliability of the mine plan can be assessed, but the computational complexity and time required increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-defining probability distributions for all input parameters before running simulations. The system establishes mean values, standard deviations, and bounds for each parameter based on historical data and expert judgment. This preliminary characterization of uncertainty allows the Monte Carlo simulation to efficiently sample from predefined distributions rather than requiring complex real-time calculations, significantly reducing computation time.
Solution Approach 2:
The patent creates simplified copies of the deterministic mine plan in the form of stochastic replicas. Instead of modifying the original deterministic model, the system generates multiple stochastic copies that incorporate variability. Each replica is a simplified representation that captures the essential uncertainty characteristics, allowing rapid parallel simulation while preserving the core plan structure and avoiding the need for complex computational optimizations.
3Measurement precision
If detailed variance analysis is performed on each task, then comprehensive robustness information is obtained, but the data processing and analysis burden becomes unmanageable
Solution Approach 1:
The patent merges the robustness analysis of individual tasks into aggregate work package-level metrics. Instead of analyzing variance for each discrete task separately, the system groups related tasks into work packages and aggregates their float values. This merging reduces the number of individual measurements from thousands of tasks to a manageable number of work packages, while still providing comprehensive robustness information through the consolidated robustness index that captures system-wide variability effects.
Data Source
AI summary
A computer implemented method for determining the robustness of a mine plan, the method comprising computer program code instructions, being executable by a computer for replicating a mine plan, the mine plan including one or more tasks required to perform operations in a mine; grouping the one or more tasks into one or more work packages, each work package being associated with a duration of time; determining, for the one or more work packages, a variance associated with the duration of time; applying the variance based on at least one simulation to generate a float, the float being the duration of time between the completion of a precedent work package and/or the commencement of a subsequent work package; consolidating each float from the at least one simulation into a robustness index; and displaying at least a portion of the robustness index to a user via a graphical user interface.