Tightly-Packed Repetitive Schedules: A Tetris Challenge
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
Abstract A large portion of new and rehabilitation projects for the civil infrastructure involves repetitive tasks that mandate high degree of synchronization among the resources to meet project constraints. Despite their large need for support, few tools exist commercially. This is due, in part, to the computational and visualization challenges of existing repetitive scheduling techniques, which complicate the process and make it less understood, particularly when the repetitive units are not identical and when resources are limited. This presentation introduces enhancements by dealing with repetitive scheduling as a game of Tetris, where tasks can change geometry to fit tightly together to save project duration. This is demonstrated through novel mathematical formulations, as well as a new heuristic scheduling algorithm (First-Come-First-Serve). It will be shown that the proposed geometrical adjustments are more advantageous in improving repetitive schedules than using expensive accelerations, and the new visuals make the schedule more legible. This research is a step towards making repetitive scheduling a mainstream tool, to benefit the construction industry.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it