Meta Partial Benders Decomposition for the Logistics Service Network\n Design Problem
Bibliographic record
Abstract
Supply chain transportation operations often account for a large proportion\nof product total cost to market. Such operations can be optimized by solving\nthe Logistics Service Network Design Problem (LSNDP), wherein a logistics\nservice provider seeks to cost-effectively source and fulfill customer demands\nof products within a multi-echelon distribution network. However, many\nindustrial settings yield instances of the LSNDP that are too large to be\nsolved in reasonable run-times by off-the-shelf optimization solvers. We\nintroduce an exact Benders decomposition algorithm based on partial\ndecompositions that strengthen the master problem with information derived from\naggregating subproblem data. More specifically, the proposed Meta Partial\nBenders Decomposition intelligently switches from one master problem to another\nby changing both the amount of subproblem information to include in the master\nas well as how it is aggregated. Through an extensive computational study, we\nshow that the approach outperforms existing benchmark methods and we\ndemonstrate the benefits of dynamically refining the master problem in the\ncourse of a partial Benders decomposition-based scheme.\n
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".