The quality‐constrained scheduling problem in plastics compounding
Bibliographic record
Abstract
Abstract Interest is increasing in plastic compounding plants that offer tailor‐made resins. Such plants produce a wide range of products in small quantities and with frequent changeovers. The underlying scheduling problem has been extensively researched; however, the concept of incorporating qualities of the finished product in the problem of plastics compounding has not been considered. We express product qualities as an additional problem constraint so that the production schedule ensures product quality. The additional constraint makes this mixed integer nonlinear program (MINLP) problem more difficult to solve. Several case studies are solved to illustrate the utility of the proposed approach. Experiments demonstrated that qualities of the finished product can be ensured a priori if the appropriate relations are developed and integrated in the optimisation model. As well, this paper provides insight into the economic aspects of the scheduling problem under consideration. Experiments showed that none of the cost components (operation, raw material, inventory, penalty or utilities) can alone play the role of the optimisation criterion. © 2012 Canadian Society for Chemical Engineering
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".