Improved adaptive genetic algorithm for dynamic multi-specification one-dimensional cutting problem
Bibliographic record
Abstract
Rebar is an essential material in the construction of bridges and houses. Rebar cutting is an important link in rebar processing, but it is usually completed by manual experience, which is not only time-consuming, but also causes serious waste and reduces the economic benefits. As the country vigorously promotes the green construction method, the traditional rebar cutting method is difficult to meet the development requirements. So dynamic multi-specification one-dimensional cutting problem is studied in this paper. A mathematical model aiming at the maximum utilization rate of raw material is established, and an improved adaptive genetic algorithm is proposed. Large-scale, small-scale, single-specification masterbatch and multi-specification masterbatch are selected for simulation experiments, respectively. The results show that the proposed algorithm can deal with both large- and small-scale multi-specification or single-specification masterbatch cutting problems. Moreover, the algorithm has good performance in solving accuracy and convergence speed, which verifies its feasibility, effectiveness, and stability. Finally, aiming at the problem of dynamic insertion of orders, one-dimensional cutting software is developed, rapid and real-time cutting of rebars is realized, and the utilization rate of building rebars is improved, which plays a positive role in promoting the high-quality development of construction industries such as bridges.
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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 | 0.000 |
| 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".