Addressing Orientation Symmetry in the Time Window Assignment Vehicle Routing Problem
Bibliographic record
Abstract
The time window assignment vehicle routing problem (TWAVRP) is the problem of assigning time windows for delivery before demand volume becomes known. This implies that vehicle routes in different demand scenarios have to be synchronized such that the same client is visited around the same time in each scenario. For TWAVRP instances that are relatively difficult to solve, we observe many similar solutions in which one or more routes have a different orientation, that is, the clients are visited in the reverse order. We introduce an edge-based branching method combined with additional components to eliminate orientation symmetry from the search tree, and we present enhancements to make this method efficient in practice. Next, we present a branch-price-and-cut algorithm based on this branching method. Our computational experiments show that addressing orientation symmetry significantly improves our algorithm: The number of nodes in the search tree is reduced by 92.6% on average, and 25 additional benchmark instances are solved to optimality. Furthermore, the resulting algorithm is competitive with the state of the art. The main ideas of this paper are not TWAVRP specific and can be applied to other vehicle routing problems with consistency considerations or synchronization requirements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".