Coordinated Multistage Scheduling of Parallel Batch-Processing Machines Under Multiresource Constraints
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
Motivated by scheduling challenges of burn-in ovens in back-end semiconductor manufacturing, we propose a linear-programming-based algorithm, an integer-programming-based algorithm, and a heuristic-based algorithm to schedule nonhomogenous parallel batch machines with nonidentical job sizes and incompatible job families. We consider the common scheduling of consecutive steps that are linked together through secondary scarce resources. Our approach addresses the availability and compatibility of several resources required to make each process possible. The algorithms strive to meet short-term production targets expressed by product and step. The algorithms are shown to be effective and computationally efficient for this purpose. Taken together with previously developed methodology for the practical translation of target output schedules into short-term local production targets, this article suggests how a complex supply chain manufacturing system can be efficiently and effectively managed by decentralized local scheduling algorithms striving to meet short-term production targets that in turn ensure maintenance of an appropriate dynamic profile across production steps for work-in-process.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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