Hybrid heuristic for the one-dimensional cutting stock problem with usable leftovers and additional operating constraints
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
The One-Dimensional Cutting Stock Problem consists in cutting long bars into smaller ones, to satisfy customers’ demand, minimizing waste and cost. In this paper the standard problem is extended with the inclusion of additional constraints that are generally neglected in scientific literature, although relevant in many industrial applications. We also modified the standard objective function, by assuming that bars may have a different economical value and a different processing or shipping priority. Moreover, in line with business requirements, among solutions that generate the same cutting waste, we prefer the ones that generate a low number of leftovers, especially if leftovers are long, so that the likelihood of their reuse is high. To solve the problem, we propose a Simulated Annealing based heuristic, which exploits a specific neighbor search. The heuristic is implemented in a parametric way that allows the user to set the priorities of the bars and to choose the specific sub-set of constraints he or she wants to consider. The heuristic is finally tested on many problem instances, and it is compared to three benchmarks and to one commercial software. The outcomes of this comparative analysis demonstrate both its quality and effectiveness.
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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