An integrated stochastic EPQ model under quality and green policies: generalised cross decomposition under the separability approach
Bibliographic record
Abstract
In this paper, a bi-objective multi-product constrained and integrated economic production quantity model is designed by considering the quality control and green production policies. The aforementioned model comes with stochastic constraints. Moreover, to create a kind of green approach policy, tax cost of greenhouse gas emissions and limitations are considered. The aim of this study is to optimise the total inventory cost and the total profit, while the stochastic constraints are satisfied. Due to the inconsistency of objectives, an Lp-metric function is utilised to integrate and achieve a single objective function. Therefore, the mathematical formulation of the problem is bi-objective stochastic mixed integer nonlinear programming large scale and hard to solve. Accordingly, generalised cross decomposition under the separability approach is utilised as an effective algorithm for global optimisation. Moreover, sensitivity analysis revealed that increasing the cost function weight versus decreasing the profit function weight leads to the change rate of the integrated-objective function becomes positive with a steep slope.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".