Inventory pinch based, multiscale models for integrated planning and scheduling‐part I: Gasoline blend planning
Bibliographic record
Abstract
A two‐level algorithm to compute blend plans that have much smaller number of different recipes, much shorter execution times, and the same cost as the corresponding multiperiod mixed‐integer nonlinear programming is introduced. These plans become a starting point for computation of approximate schedules, which minimize total number of switches in blenders and swing tanks. The algorithm uses inventory pinch points to delineate time periods where optimal blend recipes are likely constant. At the first level, nonlinear blend models are optimized via nonlinear programming. The second level uses fixed recipes (from the first level) in a multiperiod mixed‐integer linear programming to determine optimal production plan followed by an approximate schedule. Approximate schedules computed by the multiperiod inventory pinch algorithm in most of the case studies are slightly better than those computed by global optimizers (ANTIGONE, GloMIQO) while requiring significantly shorter execution times. Such schedules provide constraints for subsequent detailed scheduling in Part II. © 2014 American Institute of Chemical Engineers AIChE J , 60: 2158–2178, 2014
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".