Optimal Control Policy of Unreliable Production Systems Generating Greenhouse Gas Emission
Bibliographic record
Abstract
The current paper addresses the production planning problem of unreliable manufacturing systems generating greenhouse gas (GHG) emissions, producing a single product type in a dynamic and stochastic context. This work aims to develop a control policy that minimizes the sum of backlog, inventory, and emission costs. To achieve this goal, the stochastic optimal control theory is used to develop the optimality conditions solved by numerical techniques to establish the control policy structure. Sensitivity analyses are provided to depict and validate the obtained structure of the production policy characterized by multiple thresholds, which regulate the production rate with the emission and inventory levels. Furthermore, the performance of the developed optimal control policy is compared with the most pertinent ones identified in the literature. The developed optimal control policy outperformed those in the literature by significantly reducing the total cost incurred by these policies. Finally, the developed control policy is implemented to equip the manager of the considered manufacturing system with a practical 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".