Large-scale capacitated part-routing in the presence of process and routing flexibilities and setup costs
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
We develop a Lagrangean relaxation-based heuristic procedure to generate a near-optimal solution to large-scale capacitated part-routing problems through a cellular manufacturing system with both routing flexibilities and setup times. Several alternate process plans exist for each product. Any given operation can be performed on alternate machines at different costs. The part demands can be satisfied from internal production or through outsourcing. The objective is to minimize the total material handling, production, outsourcing, and setup costs, subject to satisfying all the part demands and not exceeding any of the machine capacity limits. Our computational experiments show that large problems involving several thousand products and decision variables can be solved in a reasonable amount of computer time to within 1% of their optimal solutions. The proposed procedure is general enough to be applied directly or with slight modifications to real-life, industrial-sized problems.
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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 | 0.002 | 0.001 |
| 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