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Easily integratable to your preferred package through CSV files
Easily integratable to your preferred package through CSV files
Stochastic mine planning addresses uncertainty by optimizing decision-making in the face of varying and unpredictable conditions. Unlike traditional methods that assume fixed parameters, this approach utilizes probabilistic models to account for fluctuations in ore quality and distribution. By incorporating different scenarios and assessing risks more comprehensively, stochastic mine planning enhances decision-making, leading to improved efficiency and profitability in mining operations.
Example of Grade and Cumulative NPV report for stochastic model with MiningMath.
Stochastic model otpimization employs various advanced techniques to address uncertainties in mining operations. Several academic approaches have been explored to improve scheduling outcomes. For example, simulated annealing refines schedules by testing multiple options and adjusting based on probabilistic results. Stochastic mathematical programming assesses different scenarios to balance profit and risk, while multistage stochastic programming and heuristic methods enhance schedules by addressing uncertainties related to ore quality and mining conditions.
Despite their advantages, these methods have notable drawbacks. For example, simulated annealing and stochastic mathematical programming often require significant computational resources, leading to lengthy processing times, which can be challenging for large-scale operations. Furthermore, these techniques have not yet been widely adopted in commercial mine optimization software, with MiningMath being the only one offering a solution for stochastic model optimization.
MiningMath’s approach to stochastic mine planning involves using stochastic simulations to address geological uncertainties, such as variations in ore grade and volume. Instead of running single scenarios independently, MiningMath integrates all possible scenarios simultaneously with an adapted resource block model and a diverse range of workflows. This adapted resource model includes equiprobable values for variables with inherent uncertainty. As a result, MiningMath generates detailed reports that provide a comprehensive risk profile, showing key indicators such as minimum, maximum, expected values, and percentile thresholds (P10 and P90), which reflect the range and distribution of potential outcomes.
MiningMath’s approach to stochastic mine planning scheduling stands out due to its comprehensive integration of all potential scenarios into a unified model. This thorough and integrated approach allows for better decision-making and risk management, ultimately leading to more robust and reliable mine planning compared to methods that analyze scenarios in isolation.
Windows 64-Bit (x86_64) - 121 MB
Windows 64-Bit (x86_64) - 121 MB
With constant developments since 2013, MiningMath has reached a mature and robust state. We are the first and only single-step mining optimization engine available in the market!
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