Joint optimization of maintenance and production scheduling for unrelated parallel-machine system
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
This paper investigates integrated maintenance and production scheduling in an unrelated parallel-machine production environment. We address three different preventive maintenance (PM) policies, namely (1) PM at fixed predefined time intervals, (2) optimal PM period that maximizes machines' availability, and (3) optimal PM period that maintains a minimum reliability threshold for a given production period t. We incorporate the three PM policies into a mixed-integer mathematical model that jointly optimizes maintenance and production scheduling. The objective is to minimize maximum completion time and maintenance costs. The weighted-sum method is adopted to joint these two objectives as they have different units. Due to the complexity of the proposed model, a problem-specific designed simulated annealing (SA) algorithm is used to solve it. The effect of the three adopted maintenance policies on the delivery times of products and cost of maintenance activities is illustrated through a small case study.
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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 | 0.001 |
| 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