Saving based algorithm for multi-depot version of vehicle routing problem with simultaneous pickup and delivery
Why this work is in the frame
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Bibliographic record
Abstract
The paper presents saving based algorithm for the multi-depot version of VRPSPD. We developed four saving based algorithms for the problem. These algorithms are: 1) partition based algorithm; 2) nearest depot algorithm; 3) saving algorithm; 4) Tillman's saving algorithm. In the saving heuristics, a new route is created by merging two routes. Checking the feasibility of new route obtained after merging two routes is difficult because of the fluctuating load on the route. We use cumulative-net pick approach for checking the feasibility when two existing routes are merged. Numerical experiment is performed on benchmark problem instances available in literature. The numerical results show that the performance of the proposed heuristics is qualitatively better than the existing insertion based heuristics.
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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