A simulated annealing with multiple‐search paths and parallel computation for a comprehensive flowshop scheduling problem
Bibliographic record
Abstract
Abstract Recent studies have demonstrated that the performance of a simulated annealing algorithm can be improved by following multiple‐search paths and parallel computation. In this paper, we use these strategies to solve a comprehensive mathematical model for a flexible flowshop lot streaming problem. In the flexible flowshop environment, a number of jobs will be processed in several consecutive production stages, and each stage may involve a certain number of parallel machines that may not be identical. Each job has to be split into several unequal sublots by following the concept of lot streaming. The sublots are to be processed in the order of the stages, and sublots of certain products may skip some stages. This complex problem also incorporates sequence‐dependent setup times, the anticipatory or nonanticipatory nature of setups, release dates for machines, and machine eligibility. Numerical examples are presented to demonstrate the effectiveness of lot streaming in hybrid flowshops, the performance of the proposed simulated annealing algorithm, and the improvements achieved using parallel computation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".