An optimal construction resource leveling scheduling simulation model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it