Algorithm for Scheduling with Multiskilled Constrained Resources
Why this work is in the frame
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Bibliographic record
Abstract
Scheduling with constrained resources, particularly skilled labor, is a major challenge for almost all construction projects. In the literature, various techniques have been developed to reduce consequent project delay of constrained resources. Most of these techniques assume single-skilled resources and use heuristic rules to decide which activity will receive the resource first and which ones to delay. To improve existing solutions, this paper introduces some modifications to heuristic resource-scheduling solutions, considering multiskilled resources. The proposed approach stores and utilizes information about the resource(s) that can be substituted when there is a shortage in another one. Using this information, less utilized resources can be combined to substitute the shortages in constrained resources during the shortage period, taking into consideration the loss in work productivity. To automate the proposed algorithm, a macroprogram has been written on a commercial scheduling software. An example application is presented to show the improved results of the proposed approach over existing heuristics. The use of the proposed approach as a better resource management tool within the construction industry is then discussed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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