Dispatching Rules for Minimizing Deviation from JIT Schedule Using the Earliness -Tardiness Scheduling Problem with Due Windows Approach
Why this work is in the frame
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Bibliographic record
Abstract
In Earliness-Tardiness (E/T) scheduling approach, the Just-In-Time (JIT) schedule is a schedule with zero earliness and zero tardiness. However, this is an optimal schedule and even notional in some instances where tardiness and earliness are inevitable. However, minimizing the deviation at the upper region (tardiness) and the lower region (earliness) from the JIT schedule is a challenge. This work proposes solutions. Two proposed heuristics; TA1 and TA2 as well as some existing heuristics were explored to solve simulated problems ranging from 5≤n≤400 and the results obtained were benchmarked against the JIT schedule. The results obtained show that one of the heuristics, TA2 yielded JIT schedules for many problem sizes at the lower and upper deviation than other solution methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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