A Computational Analysis Of Balanced Jit Optimization Algorithms
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
The balanced schedule problem in mixed-model, JIT manufacturing is examined. Solving this problem is the cornerstone of production in any JIT facility. Although very efficient procedures have been demonstrated for the single-level problems, nobody has examined the nature of the solutions that these procedures actually produce. In this paper, the first large scale computational study is undertaken to examine these optimization algorithms. Furthermore, several open questions and conjectures have developed in the past few years. These open questions are formulated as propositions and answered by means of the extensive computational testing. The answers to these propositions are conclusive and, in some cases, quite unexpected. While it had appeared that most of the single-level problems had been efficiently solved, this study points to several interesting questions requiring additional study.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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