Joint economic design of production, continuous sampling inspection and preventive maintenance of a deteriorating production system
Bibliographic record
Abstract
Standard continuous sampling procedures and tables are conventionally applicable only to continuous production processes that are statistically ‘in-control’. Consequently, these standards cannot be used to control quality in deteriorating production processes. Moreover, existing continuous sampling models do not consider interactions with production, inventory and maintenance aspects. In this paper, we attempt to fill these gaps in the literature. We investigate the joint design and optimization of a type-1 continuous sampling plan (CSP-1), make-to-stock production and preventive maintenance of a stochastic production system subject to both quality and reliability deteriorations. Two models of CSP-1 are considered and compared: the classical CSP-1 as in the standard procedures, and a CSP-1 plan with a stopping rule that is combined with condition-based maintenance. For both models, the optimization problem is to minimize the total incurred cost under a constraint on the outgoing quality. A combination of mathematical formulation, simulation and optimization techniques is used to solve such stochastic and constrained problems. Numerical examples are given to illustrate the resolution approach and to highlight some interesting aspects in the interactions between production, inventory, quality, maintenance and reliability. The results obtained demonstrate that sampling inspection plans realize significant cost savings compared to the 100% inspection which is commonly used in the literature of integrated models, and that using the CSP-1 with an inspection stopping rule for deteriorating processes is more cost-effective than the classical CSP-1.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".