Resource leveling in construction projects with activity splitting and resource constraints: a simulated annealing optimization
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
Overtime and over-budget construction projects are not pleasant to any stakeholder. Stakeholders want construction projects to be completed without delay and excessive cost. It is possible to meet these objectives by using resource management techniques such as resource leveling. Due to the limitation of resources and different types of them in a construction project, optimizing the resource utilization is crucial. In this paper, a meta-heuristic simulated annealing resource leveling model is presented. The novelty of this model lies not only in the type of modeling and optimization but also in its assumptions. Our model simultaneously allows activities to split and considers a limitation in resource availabilities. The developed model was implemented in a computer program. Then, it was applied to an example from the literature of resource leveling. The model successfully solved the problem. The results of our model are compared with those already available in the literature.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it