Joint maintenance and production planning optimization model for production systems with operation-dependent failure rate
Bibliographic record
Abstract
This paper investigates the issue of integrating production planning and preventive maintenance planning in a batch production context. The production planning problem is a multi-product capacitated lot-sizing problem. The production system is a single machine subject to random failures. To reduce the risk induced by failures, the cyclic preventive maintenance strategy is implemented and minimal repair is carried out at failure. It is assumed that both maintenance actions reduces the production capacity of the system. In the present paper, the failure rate of the system is assumed to be operation-dependent, i.e. the type of item to be processed does affect the production system’s failure rate, which in turn impacts both production and maintenance decisions. The objective is to develop a mathematical programming model to derive an integrated production and maintenance plan that minimizes the expected total costs during a finite planning horizon. An exact solution is derived for a small-size of the formulated problem. To deal with large-size problems, a genetic algorithm is developed as a solution technique. Numerical experiment is then conducted and the obtained results are discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".