An efficient hybrid algorithm for the two-machine no-wait flow shop problem with separable setup times and single server
Why this work is in the frame
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Bibliographic record
Abstract
We consider the two-machine no-wait flow shop problem with separable setup times and single server side constraints, and makespan as the performance measure. This problem is strongly NP-hard. A mathematical model of the problem is developed and a number of propositions are proven for the special cases. Furthermore, a hybrid algorithm of variable neighbourhood search (VNS) and Tabu search (TS) is proposed for the generic case. For evaluation, a number of test problems with small instances are generated and solved to optimality. Computational results show that the proposed algorithm is able to reproduce the optimal solutions of all of the small-instance test problems. For larger instances, proposed solutions are compared with the results of the famous two-opt algorithm as well as a lower bound that we develop in this paper. This comparison demonstrates the efficiency of the algorithm to find good-quality solutions. [Received 25 November 2009; Revised 26 February 2010; Revised 19 March 2010; Accepted 20 March 2010]
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it