Dynamic Allocation of Unionized Skilled Trades in Multi-Project Reactive Scheduling Context: Uber-Inspired Application Framework
Why this work is in the frame
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Bibliographic record
Abstract
Concurrent construction projects are usually distributed across various geographical locations, each generating labor resource demand for various skilled trades over its project duration. Coupled with the limited and unpredictable availability of skilled trades, this creates a dynamic situation that necessitates reliable and adaptive resource-constrained project scheduling. The management of labor unions needs to cope with this challenge in allocating skilled trades per project demands while addressing constant changes that cause delays, cancellations, or idle time. Additionally, non-unionized workers from open shops who are ready to work can be scheduled to meet the demands. Uber, a ride-hailing service provider, has effectively tackled an analogous scheduling problem. Available drivers are driving in the city, waiting for calls, and signing out whenever they choose. Orders can be placed anytime, and trip information is unpredictable. With drivers’ statuses dynamically updated, Uber utilizes proprietary algorithms to assign orders to drivers based on specific optimization rules. Inspired by the Uber approach, this paper aims to develop a conceptual framework to support union managers in (1) updating the status of trades, projects, and activities; (2) setting various optimization objectives; and (3) rescheduling within a short timeframe. Three application scenarios are postulated to account for the decision support needs of union managers in project planning.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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