Maintenance/production planning with interactive feedback of product quality
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
Purpose This paper seeks to develop an optimal stochastic control model where interactive feedback consists of the quantity of flawless and defective products. The main objective of this study is to minimize the expected discounted overall cost due to maintenance activities, inventory holding and backlogs. Design/methodology/approach The model differs from similar research projects in that, instead of age‐dependent machine failure, it considers only defective products as feedback into the optimal model for maintenance and production planning. In this paper a near optimal control policy of the system through numerical techniques is obtained. Findings In this paper, a new model in which the system's retroaction is the quantity of defective products is presented, considering that defective products are a consequence of global manufacturing system deterioration. Instead of taking into account machine failure and human error separately, it considers a defect in product as being the consequence of a combined failure; this consideration allows one to be more realistic by merging all failure parameters into a single one. A new stochastic control model, which focuses on defective products, inventory, and backlog, has been developed. Research limitations/implications This approach extended the concept of hedging point policy to the quantity of defective products combined with preventive and corrective maintenance strategies. The control policy obtained has a bang bang structure and is completely known for given parameters. Originality/value The integration of maintenance and production strategies has been mainly focused on the machine. Many research projects have been focusing on the age when dealing with machine failure. It is considered as the main target of the cost reduction in maintenance engineering departments. The originality of this paper is the taking into account of all operational failures into the same optimization model. It brings a value added to high level of maintenance and for operation managers who need to consider all failure parameters before taking decisions related to cost.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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