Joint Emission-Dependent Optimal Production and Preventive Maintenance Policies of a Deteriorating Manufacturing System
Bibliographic record
Abstract
This paper addresses the problem of joint production and preventive maintenance (PM) planning of a deteriorating manufacturing system generating greenhouse gas (GHG) emissions. The system is composed of a deteriorating machine, subject to random failures and repairs evolving in a dynamic and stochastic context. The main objective is to develop control policies that minimize the sum of backlog, inventory, maintenance, and emission costs. The stochastic optimal control theory based on the dynamic programming approach is used to obtain the optimality conditions and the optimal control policies, which are determined using numerical methods. Sensitivity analyses are provided to depict and validate the obtained structure of the production and PM policies characterized by multiple thresholds that jointly regulate the production and PM rates with the age, emissions, and inventory levels. Furthermore, we compared the performance of the obtained control policies with that of the most relevant policies found in the literature and showed their superiority by considerable cost savings. Finally, the proposal’s implementation is provided to equip managers of the considered manufacturing system with an effective and robust decision-support tool.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".