Joint Optimization of Jobs Sequence and Inspection Policy for a Single System With Two-Stage Failure Process
Why this work is in the frame
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Bibliographic record
Abstract
We will discuss the joint optimization of the jobs sequence as well as inspection policy for a single system expected to process n jobs with different processing times. The system has a two-stage failures process, i.e., first a defect arises in the system, and if the defect is not detected, the system eventually fails. The interrupted job due to failure should be restarted after corrective replacement of the system. The possibility of inspecting the system before starting a job is considered to detect a potential defect. We develop two models to find the optimal policy based on either total expected makespan, or total expected cost. In the cost optimization model, we assume a common due date for all jobs and incur a penalty cost per unit time that the makespan exceeds the due date. We develop a recursive formula to obtain the expected makespan, and the number of failures and preventive replacements and present the application of our proposed models to a system, which is supposed to process four jobs. We compare the results of the direct calculation (recursive formula) with a Monte Carlo simulation model, and discuss how changing the models' parameters can impact the optimal policy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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