A Collaborative Search Strategy to Solve Combinatorial Optimization and Scheduling Problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Various scheduling problems that occur in manufacturing industries have been investigated in the literature. They are inherently complex and often referred to as combinatorial NonPolynomial (NP) hard problems. These problems are very difficult to solve using existing heuristics or conventional techniques. This chapter presents a generic framework of a collaborative search algorithm to solve scheduling problems. The proposed framework contains two independent search modules that exchange information while consecutively run to solve a problem. Based on the proposed framework a search algorithm tailored for the flow shop scheduling is presented. The computational results for the two challenging classical problem sets clearly indicate the superior performance of the proposed method over several conventional techniques including a simulated annealing, a genetic algorithm and a hybrid genetic algorithm. The CSA results also compare favourably with those of the two newly developed algorithms, PSO (Liao et al., 2007) and SAMED-FSS (Azizi et al., 2009b).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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