Multi-objective optimization and simulation of earthwork equipment configuration in the upper reservoir of a pumped storage power station
Bibliographic record
Abstract
To find the optimal equipment configuration for the earthwork construction in the upper reservoir of pumped storage power stations, the discrete event simulation was combined with the multi-objective optimization to optimize the construction equipment configurations of the upper reservoir in this paper. According to the daily filling intensity, different types and numbers of equipment were combined and the daily construction time of various combinations was calculated through simulation. Then, the equipment costs and carbon emissions were calculated by a multi-objective model. Setting different weights for each objective to consider the decision preferences of decision-makers, the optimal equipment configuration of earthwork construction was obtained by the multi-objective decision. This optimization framework was applied to a reservoir engineering in karst depressions, the results show that different preferences have an impact on the decision of optimal schemes. At the same time, the obtained schemes show low average queuing trucks and road congestions, and a high utilization rate of the equipment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".