Coordinated Multistage Scheduling of Parallel Batch-Processing Machines Under Multiresource Constraints
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".