Application of simulation and optimization to support mine plan execution

Suboptimal decisions introduced during the mine planning process are generally a consequence of both a myopic view of planning objectives as well as time and resources available to convert longer-term plans into execution plans. Erosion of strategic mine plan value in conversion to execution plans is likely to amount to billions of dollars each year. When integrated with operations data feeds, simulation and optimization tools can be used to preserve the value in the strategic mine plan. Optimization models are capable of both automating production of execution plans and improving performance. This reduces the manual burden of revising plans while improving alignment with long-term production objectives. Simulation models can also support the process of developing effective execution plans by providing forward visibility of mine operations performance. They can also be used to test the robustness of optimized execution plans and response to operational variability and disturbances.

A high-performance mining operation maximizes value by optimizing the mining strategy and life-of-mine (LoM) plan and then aligning execution with the strategic plan. The mine planning process spans a spectrum from strategic (LoM) planning to real-time task allocation. Periodic revisions occur at different planning levels, with tactical and operational details added to the plan. However, some plan attributes are typically carried forward from long-term plans into execution, such as cut-off grades and material destination assignments. Variations in actual mining task completion times relative to planned times can alter the relative timing of mining tasks, causing task and equipment delays. An example of this is shown in Fig. 1. Without a mechanism for refining execution plans in response to operational variability, mining operations are at risk of schedule creep, resulting in variations in ore-to-waste ratios, reduced production and misalignment with the strategic plan.

Typical approaches for managing mine operations variability include the addition of lags between tasks, and management of progress for critical tasks through monitoring and taking measures to avoid late completion and knock-on effects. An improved response to operational variability is possible if the execution plan can be revised quickly in response to operational changes, without negative impacts to the objectives of the strategic plan.

Used together, optimization and simulation models can provide a structured approach for rapidly adapting execution plans:

• Optimization models support execution plan adjustments by determining an optimal schedule given constraints such as task dependencies and objectives.
• Simulation models estimate the range of likely mining operation performance outcomes based on actual conditions, enabling early identification of potential issues and evaluation of alternative plans before execution.
• Integration of simulation and optimization allows for rapid evaluation of responses to deviations between quarterly mine plans and real-time operational data, ensuring that adjustments align with strategic objectives before implementation of the execution plan. Figure 2 provides an illustration of this process.

Successful implementation of optimization and simulation tools for execution planning requires mature data systems capable of capturing real-time operational states, as well as automated processes for data integration and model execution.

Performance improvements observed in case studies with optimization and simulation supported planning include:

• Reduction in schedule creep — Execution plans dynamically adapt to maintain alignment with production objectives, reducing cumulative delays.
• Enhanced ore management of ore-waste ratios — Real-time adjustments to material destinations mitigate variations in ore-to-waste ratios and ore grade inconsistencies.
• Increased haulage throughput — Optimized routing, strategic balancing of short-haul and long-haul cycles, and dynamic truck allocation reduce idle times and improve haulage efficiency.
• Cost savings — Automation of much of the planning process reduces planning turnaround time and minimizes losses associated with unoptimized execution.

A mining operation that can rely on an optimal response to operational variability that preserves the value of the strategic plan will significantly outperform traditional planning processes. Integrated optimization and simulation can support the mine planning process to respond to operational variability, improving alignment with long-term planning objectives. Effective deployment of simulation and optimization tools requires reliable operational data feeds, well-defined planning constraints, and organizational adoption of adaptive planning methodologies. Once integrated, these tools significantly accelerate execution planning processes, reduce manual planning effort and optimize responses to changing mine conditions. As the industry moves toward greater automation and data-driven decision-making, the role of simulation and optimization in mine planning will continue to expand, driving increased efficiency and cost savings.

A list of all references is available in the full paper.

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Mining, Metallurgy & Exploration (2024) 41:3789–3800, https://doi.org/10.1007/s42461-024-01118-8