Incentive genetic algorithm based time–cost trade-off analysis across a build–operate–transfer project concession period
Why this work is in the frame
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Bibliographic record
Abstract
The build–operate–transfer (BOT) scheme is widely applied to finance new infrastructure projects with private sector (concessionaire) participation. For a predetermined concession period (CP), assuming that CP consists of the construction duration (CD) and the concession operation period (OP), different construction durations result in different profits for the concessionaire. Meanwhile, according to the time–cost trade-off (TCT) principle, shortening the CD increases the construction cost; shortening the CD also prolongs the OP, which could increase the total benefit of BOT projects. Hence, how to arrange construction reasonably to maximize the whole profit is a key issue for a concessionary. This paper proposes a methodological framework including optimization, sensitivity analysis, and improved (incentive) genetic algorithms (GA) for BOT projects. Through the proposed methodological framework, the reasonable construction duration of a BOT project can be obtained. A numerical example is used to verify the proposed methodology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it