An exact approach to the integration of noncyclical preventive maintenance scheduling and production planning for a series
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we generalise a model for integrated non-cyclical preventive maintenance scheduling and production planning from a single machine to a series-parallel production line. As for a single machine, we consider a set of products that must be produced in lots during a given time horizon. The maintenance strategy involves possible preventive replacements at the beginning of each maintenance period and minimal repair at machine failure. The model, an integer linear program, determines the optimal production plan and preventive replacement for each machine of the production line. The objective is to minimise the total cost (preventive and corrective maintenance costs, setup costs, holding costs, backorder costs, and production costs) while meeting the demand for each product over the horizon. We performed experiments using CPLEX 12.5.1, and almost all instances were solved within five minutes with a reasonable gap.
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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 | 0.005 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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