Decision Making in Flexible Mine Production System Design Using Real Options
Bibliographic record
Abstract
Large multifaceted capital projects, such as those in the mineral resource industry, are often associated with diverse sources of both internal and external risks and uncertainties. Risks can cause delays to the planned schedule of a project, add a significant cost, and greatly influence its profitability. Uncertainties can be associated with project risks, as well as with opportunities that can develop throughout the project’s lifecycle. Having the ability to plan for these uncertainties, by incorporating flexible alternatives into the system design, is increasingly recognized as critical to long-term corporate success. This paper advances the knowledge needed to incorporate flexibility in systems engineering and management for both practitioners and researchers. Flexibility is defined in this paper as the ability of a system to sustain performance, preserve a particular cost structure, adapt to internal or external changes in operating conditions, or take advantage of new opportunities that develop during a mine’s life cycle by modifying operational parameters. By engaging in planning for flexible production systems, the effects of risk on a particular project value can be examined, project volatility can be calculated, and potential flexible mining alternatives can be evaluated. Once identified, a real options valuation provides a strategic decision-making tool for mine planners to determine the value of incorporating flexible alternatives into the mine plan. This paper demonstrates that flexibility can become an equal partner among the parameters controlling the decision-making process for underground engineering construction systems, followed by industry practitioners. It presents a methodology in mine production system design by introducing flexibility into design through the application of real options valuation techniques. Real world case studies related to flexible planning and design of construction and production systems in underground hard rock mines are presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".