Selective pricing in branch-price-and-cut algorithms for vehicle routing
Why this work is in the frame
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Bibliographic record
Abstract
Branch-price-and-cut is a leading methodology for solving various vehicle routing problems (VRPs). For many VRPs, the pricing subproblem of a branch-price-and-cut algorithm is highly time consuming, and to alleviate this difficulty, a relaxed pricing subproblem is used. In this paper, we introduce a new paradigm, called selective pricing, that can be applied in this context to reduce the time required for solving hard-to-solve VRPs by branch-price-and-cut. This paradigm requires the development of a labeling algorithm specific to the pricing subproblem. To illustrate selective pricing, we apply it to a branch-price-and-cut algorithm for the VRP with time windows, where the relaxed pricing subproblem is a shortest ng-path problem with resource constraints. We develop a labeling algorithm for this subproblem and show through computational experiments that it can yield significant time reductions (up to 32%) to reach a good lower bound on certain very-hard-to-solve VRPTW instances with 200 customers. We also introduce a new labeling heuristic which also leads to computational time reductions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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