An optimal periodic scheduler for dual-arm robots in cluster tools with residency constraints
Bibliographic record
Abstract
Discusses a scheduling technique, for cluster tools, that addresses postprocessing residency constraints and throughput requirements. The residency constraints impose a limit on the postprocessing time that a material unit spends in a processing module. The technique searches in the time and resource domains for a feasible schedule with a maximum throughput. It operates in two main phases; the initial one of which (and the lower complexity one) computes a simple periodic schedule. For a large number of problem instances, the simple periodic schedule feasibly solves the problem. If a feasible schedule cannot be found in the first phase, the scheduler enters phase two (the higher complexity one) to compute a feasible schedule. During this phase, the scheduler incrementally increases the period only if necessary, to keep the throughput at a maximum. Several heuristics are designed and added to reduce the complexity of the scheduling algorithm. The resulting schedules are deadlock free, since resources are scheduled according to the times that they are available. Analytical and experimental analyses demonstrate the correctness and efficiency of our proposed technique.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".