Multicut logic‐based Benders decomposition for discrete‐time scheduling and dynamic optimization of network batch plants
Bibliographic record
Abstract
Abstract This study presents the first application of a logic‐based Benders decomposition (LBBD) technique in the field of simultaneous scheduling and dynamic optimization (SSDO), applied to network batch processes with a discrete‐time scheduling formulation. The proposed algorithm employs neighborhood information of ordered discrete decisions (e.g., batching variables) to generate cuts, rather than relying on traditional cut generation techniques based on dual information that are implemented in generalized Benders decomposition (GBD) algorithms. The proposed algorithm relies on solving multiple subproblems per iteration, which is a feature that allows the generation of multiple cuts per iteration thus producing accurate approximations of the objective function in shorter computational times. This results in the herein proposed multicut logic‐based discrete Benders decomposition (MLD‐BD) algorithm, which enables features such as a pruning strategy, and a cut‐off technique. Two case studies are used to demonstrate the computational advantages of the MLD‐BD framework against GBD and heuristic methodologies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".