A hybrid delayed differentiation multiproduct EPQ model with scrap and end-products multi-shipment policy
Bibliographic record
Abstract
The present work intends to optimize a hybrid delayed differentiation multiproduct economic production quantity-EPQ model with the scrap and end-products multi-shipment policy. Since the requirements of multi-goods have a standard part in common, our fabrication planning adopts a two-phase delayed differentiation strategy to make the standard components first and produce the finished multi-goods in the second phase. Implementing a partial subcontracting option (with the additional expense) for the standard parts helps us to expedite the required uptime in the first phase. A screening process identifies the faulty items that need to be removed to ensure the in-house production quality. A multi-shipment plan delivers the finished lot of end-products to clients in fixed time intervals. This study optimizes the overall operating expenses of this intra-supply chain system, including fabrication, delivery, and client stock holding, through our proposed modeling, formulation, and optimization procedure. In addition, this study gives a numerical demonstration of the obtained results’ applicability and usefulness to managerial decision-making.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".