Multi-item fabrication-shipment decision model featuring multi-delivery, postponement, quality assurance, and overtime
Bibliographic record
Abstract
The study applies a postponement strategy to a multi-item fabrication-shipment decision making in a vendor-buyer coordinated environment with multi-delivery, quality reassurance, and overtime. To cope with the recent client demand trend asking for rapid response, quality, and diversified goods, today’s manufacturers require a multi-item production-shipping scheme to satisfy customers’ needs in cost-saving, quality, and timely matter. In our model, we first produce all needed mutual components and postpone manufacturing of finished goods in the second phase. To expedite mutual parts’ fabrication time, overtime is used. Product quality is reassured through screening the defeats and reworking repairable defectives in both fabrication phases. To decide the optimal fabrication-shipment policy, we build a math model and apply the cost minimization technique to the problem. Upon deriving the optimal policy, we utilize an example to demonstrate how our model works and its capability in exposing various previously inaccessible information to the problem. These detailed results can facilitate managerial decision-making and boost the performance of such a specific multi-item postponement fabrication-shipment system in cost-saving, product quality, and timely response.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".