Production scheduling optimisation using mixed integer programming with machine learning dilution prediction capabilities for underground open stoping operations

For decades, Mixed Integer Programming (MIP) has been successfully utilised to optimise production schedules in underground mining, with increasingly notable results reported. However, recurrent inconsistencies between schedule forecasts and actual production due to imprecise input assumptions, such as mining dilution factors, subtly impair the robustness of optimal solutions, with detrimental hierarchical effects on the business’s cashflow projections and profitability. To address this, this study leverages emerging applications of Machine Learning (ML) and adjacent technologies that are revolutionising intelligent prediction of dilution in underground mining operations. The study proposes a synergistic nexus between MIP and ML models using ML-predicted dilution on a per-stope granularity instead of the traditional single dilution factor to improve the schedule’s forecasting accuracy. A sample of 61 stopes from an underground open-stoping operation was used to create and optimise schedules based on empirically determined and ML-predicted dilution factors. Study findings revealed a 3.1% higher net present value (NPV) for MIP-optimised schedules over manual schedules for the same dilution factor (empirical). Further, it was also noted that the ML-predicted dilution at 74% accuracy on a per-stope granularity enhances the MIP-optimised schedules’ tonnage forecast precision by at least 4 % and the NPV by at least 2 % compared to MIP-optimised schedules using the single dilution factor over a 16-month period. Additionally, results revealed that MIP schedules augmented with ML-predicted dilution demonstrated greater flexibility in navigating schedule constraints, leading to better schedule responsiveness and granularity on forecasts. Thus, the study improves optimal solutions’ robustness, reliability and production scheduling efficacy. • Machine Learning predictive models can complement mathematical optimisation models • Machine Learning predicted factors enhance robustness of optimised schedules • Granular per-stope Machine Learning inputs outperform single average-type factors