Trade-off between time and cost in project planning: a simulation-based optimization approach
Bibliographic record
Abstract
Mine development or construction projects should be carefully scheduled to meet the project objectives in terms of duration, budget, and scope since they include many highly time- and cost-sensitive activities. The inherent complexity in mining operations, coupled with material, equipment, and resource availabilities, commodity price cyclicality, and market trend uncertainties, can lead to a high risk to the project, resulting in schedule and cost overruns. Therefore, these projects must be planned and controlled efficiently to ensure that the required capital investment does not exceed the project budget and the project deadline is met. This paper proposes a simulation-based model to optimize the trade-off between time and cost of project planning problems under uncertainty. In doing so, equally probable realizations are generated considering different project duration crashing scenarios to quantify the impact of uncertainty on the total project cost and project completion time, and risks are assessed. A numerical example is provided to show the performance of the proposed approach through an underground mine development project. Statistical analysis of the results obtained from the developed simulation model identifies the risk of project completion time, the criticality of activities, and bottleneck activities of the project. In addition, the time–cost trade-off is achieved under the project deadline and budget constraints by implementing 20,736 different crashing scenarios. Finally, the results obtained from the developed formulation are compared with those obtained from the linear programming solution. The proposed approach has a strong potential to add value to project management of mining projects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".