Minimisation des surcoûts dans un contexte hors délai : cas des projets de construction dans les pays en développement
Bibliographic record
Abstract
Cost overruns are frequent in the construction industry. They become greater the more the construction project is delayed. In developing countries, this situation is recurring and constitutes a significant financial problem. In a time-overrun context, our objective is to find a method that can enable us to reduce the delay while minimizing the subsequent cost overruns. We thus developed a mathematical model named CCOMTOC (construction cost optimisation model in time-overrun context). The model was tested and results reveal that the reduction of cost overruns is effective. We showed that in a time-overrun context, according to the importance of delay penalties, we obtain two distinct situations. On the basis of the reference cost calculated for a maximum compression of tasks within the normal duration, we note that, for low delay penalties, the total cost after further delays to the estimated completion time decreases as we deviate from the initial completion time. On the other hand, for relatively high delay penalties, the cost first passes by a minimum before increasing regularly during the further delays to the estimated completion time.Key words: time overrun, optimization, linear programming, time skid, delay make-up, cost overrun.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".