Dynamic Allocation of Unionized Skilled Trades in Multi-Project Reactive Scheduling Context: Uber-Inspired Application Framework
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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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".