Dynamically scheduling and maintaining a flexible server
Bibliographic record
Abstract
Abstract Deciding how to jointly schedule jobs and perform preventive maintenance is a fundamental problem in flexible manufacturing systems, particularly those arising in semiconductor manufacturing. At the same time, past work in this area shows that, even when there is only one station and one type of job, identifying policies that minimize the amount of work‐in‐process (WIP) is a difficult problem. In this paper, we study a single‐station version of this problem with an arbitrary number of job classes, with the objective of minimizing average maintenance costs plus the weighted average amount of WIP. We identify conditions under which it suffices to schedule jobs according to both a server‐state‐dependent version of the cμ‐rule, and a static cμ‐rule where the average service rates are used. One of these conditions states that the ratio between the service rates should remain constant as the server deteriorates. When this assumption does not hold, scheduling with the cμ‐rule can in fact lead to an unstable system; we illustrate this using a simple example. On the other hand, we also present numerical evidence that cμ‐based scheduling performs well compared to other scheduling rules, and relative to a policy based on solving a Markov decision 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".