Slack-Based Techniques for Robust Schedules
Why this work is in the frame
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Bibliographic record
Abstract
. Many scheduling systems assume a static environment within which a schedule will be executed. The real world is not so stable: machines break down, operations take longer to execute than expected, and orders may be added or canceled. One approach to dealing with such disruptions is to generate robust schedules: schedules that are able to absorb some level of unexpected events without rescheduling. In this paper we investigate three techniques for generating robust schedules based on the insertion of temporal slack. Simulation-based results indicate that the two novel techniques out-perform the existing temporal protection technique both in terms of producing schedules with low simulated tardiness and in producing schedules that better predict the level of simulated tardiness. Keywords: Robustness, Uncertainty, Scheduling, Heuristics 1
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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