Maintenance/production planning with interactive feedback of product quality
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".