Optimal planning of buffer sizes and inspection station positions
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
The problem of buffer sizing and inspection stations positioning in unreliable production lines is a complex mixed integer nonlinear optimization problem. In this problem, we have a production line with n machines and n fixed-size (storage) buffers in series. The machines produce parts that are either conforming or nonconforming, and the line includes inspection stations that reject the nonconforming pats. The goal is to find the optimal buffer sizes, the number and positions of the inspection stations, and satisfy the customer demand on conforming parts while minimizing the total cost. We present in this paper an exact method to solve this complex manufacturing problem. We also present new theoretical results on buffer-size bounds, stationarity, and cost function convexity permitting to significantly reduce the problem complexity. These theoretical and algorithmic developments allow solving to optimality instances with up to 30 machines tools developed previously cannot solve.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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