Preventive maintenance and silica exposure limits integrated in the production planning of a granite processing unit
Bibliographic record
Abstract
Purpose This paper studies the integration of production and maintenance planning for an unreliable production system subject to gradual deterioration. The goal of this planning is to optimize production and maintenance while reducing workers' exposure to silica dust. The objective will therefore be to offer manufacturers a production strategy that minimizes the total cost of production while considering the health of employees. Design/methodology/approach Adequate prevention methods are determined and integrated into the granite transformation production system, which evolves in a stochastic environment. With the failure rate of the dust reduction unit being a function of its degradation state, the authors solve the optimization problem using stochastic dynamic programming in the context of nonhomogeneous Markov chain. Findings The resulting planning strategy shows that one can manage stock optimally while ensuring a healthy environment for workers. It ensures that crystalline silica prevention equipment is available and effective and defines the production rate according to a critical threshold, which is a function of the age of the dust reduction unit. Research limitations/implications This article illustrates that it is possible to integrate silica dust reduction measures into production planning while remaining optimal and ensuring the health of operators. In the present study, the machined granite was assumed to be a natural granite, and production takes place in a closed environment. Originality/value The originality of this work lies in its development of an optimal joint production and maintenance strategy, which considers limits of exposure to crystalline silica. An optimal production and maintenance control policy considering employees' health is therefore proposed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".