An optimal construction resource leveling scheduling simulation model
Bibliographic record
Abstract
To meet the physical limits of construction resources, to avoid day-to-day fluctuation in resource demands, and to maintain an even flow of application for construction resources, resource leveling is needed in the construction industry. Traditional resource leveling models assume activity durations to be deterministic. Nevertheless, activity duration may be uncertain, owing to variations in the overall environment, such as weather, site congestion, and productivity level. A new optimal construction resource leveling model is proposed in this paper, in which the combinative effects of both uncertain activity duration and resource leveling are taken into consideration. Monte Carlo simulation is used to model the uncertainties of activity duration. A searching technique using genetic algorithms (GAs) is then adopted to search for the impact of uncertain activity durations on the probabilistic optimal resource leveling indices. The model can effectively provide probabilistic optimal resource leveling indices for multiple construction resources subjected to the objective of resource leveling, and the impact of influence factors on the probabilistic resource-leveling scheduling problems.Key words: resource leveling, genetic algorithms, simulation, probabilistic scheduling.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".