A Hard-Rock Mining Company Optimizes Schedules for Strategic Decision Making

Our industry partner seeks an investment strategy and corresponding extraction schedule at daily fidelity of three-dimensional, notional blocks of ore (and waste) to maximize the discounted ounces of metal subject to spatial precedence, geotechnical, and operational constraints. This study develops an integer program, which we implement in Python, that enables fast parametric analysis, supports project-specific constraints, and generates (near-)optimal block schedules. Our model produces reliable and sustainable long-term scheduling solutions within five hours, which is faster than the time required for engineers to manually generate schedules and is acceptable for scenario analyses regarding changes in commodity price, crew productivity, plant sizing, and equipment availability. The broader implication is a 65-fold increase in scenario throughput, scaling from 30 to more than 2,000 evaluations per man-week, allowing for rapid quantification of trade-offs. In a representative operation, the tool showed diminishing returns to scale beyond 2-million-metric-tons-per-year plant capacity and identified a 1.5-million-metric-tons-per-year configuration as the most capital efficient, enabling faster, more confident evaluations of plant size, capital strategy, and operational trade-offs. The success of our optimization model demonstrates the utility of targeted in-house decision support tools for capital-intensive projects, especially when customization, scalability, and cost present challenges.

History: This paper was refereed.

Funding: Financial support from SSR Mining and the National Institute for Occupational Safety and Health [Contract 0000HCCS-2019-36404] is gratefully acknowledged.