Abstract

Open-pit mining operations are highly complex systems where production forecasting and maintaining material quality consistency are crucial for meeting production targets and ensuring the optimal performance of downstream processes. However, accurately predicting production and ensuring a stable blend of feed material into the primary crusher is challenging due to the dynamic variability in truck and shovel activities, unexpected equipment downtime, and the stochastic nature of mineral grades. In particular, unexpected shovel downtimes can cause significant disruption to production flow, leading to unbalanced material quality and throughput, which in turn impacts overall processing efficiency and recovery rates. Digital decision-intelligence solutions can be leveraged to address these challenges, enhancing the ability to navigate the inherent uncertainty and variability in open-pit mining operations. 

This study introduces an approach to simulate and optimize open-pit mining activities, utilizing the AnyLogic simulation platform to model truck movements, shovel loading, and other logistics tasks within the open-pit mine. Through detailed agent-based and discrete-event simulation, the model dynamically forecasts production rates and material movement, providing insight into the quality and volume of material arriving at the primary crusher. The simulation enables the identification of variability sources within truck and shovel activities, which are pivotal for understanding and mitigating fluctuations in production output and material characteristics. 

A primary focus of this study is the challenge of maintaining a consistent material blend (grade and hardness) despite frequent operational disruptions, such as unexpected shovel downtimes. Material blend consistency is crucial for stable processing outcomes, as it directly impacts throughput targets, crusher performance, and downstream processing efficiency. Leveraging digital decision intelligence, this framework integrates simulation with an optimization layer that continuously adapts to real-time disruptions. The optimization component recalibrates truck assignments to mitigate the impact of variability and balance blend fluctuations, ensuring the quality and quantity of feed material meet operational targets even in the face of unexpected events. 

The synergy between simulation and optimization offers a comprehensive decision-support tool for production scheduling and near real-time corrective schedule adjustments, providing production planners and dispatchers with a holistic view of production flows and quality management. By combining forecasting capabilities with proactive course correction, our approach enhances the stability and reliability of open-pit production planning, enabling mines to achieve more predictable and resilient operational outcomes. This research contributes to the growing field of digital twin technologies in mining, demonstrating how advanced digital tools can improve decision-making in complex, variable-laden environments like open-pit mining and offering a blueprint for more adaptable and efficient mining operations.