A hybrid algorithm with diversification and intensification for permutation flow shop scheduling
Bibliographic record
Abstract
This study presents a metaheuristic (SAMED) that integrates several ingredients including a simulated annealing module, three types of memory, an evolutionary operator, and a blockage removal feature in a generic framework. The SA component of the SAMED utilizes two short-term memories to intensify the search around good solutions. While the first memory is a tabu list, the second one is a seed memory list that keeps track of good solutions visited during the last iteration. Under certain condition, a long-term memory is setup by adding the best solution in the seed memory to a population list. Once the entire population is assembled, individuals are combined via an evolutionary operator to generate a new population from which an offspring might be selected as an initial solution for the subsequent iteration. The blockage removal feature is used to solve possible deadlock situations that may occur during the search procedure. The performance of the SAMED is evaluated using the well known flow shop scheduling benchmark problems of Taillard. The computational results clearly show the efficiency of the SAMED algorithm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".