Joint maintenance and production planning optimization model for production systems with operation-dependent failure rate
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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