Branch-and-Price for Personalized Multiactivity Tour Scheduling
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a branch-and-price approach to solve personalized tour-scheduling problems in a multiactivity context. Two formulations are considered. In the first, columns correspond to daily shifts that are modeled with context-free grammars, and tours are assembled in the master problem by means of extra constraints. In the second formulation, columns correspond to tours that are built in a two-phase procedure. The first phase involves the composition of daily shifts; the second assembles those shifts to generate tours using a shortest path problem with resource constraints. Both formulations are flexible enough to allow different start times, lengths, and days-off patterns, as well as multiple breaks and continuity and discontinuity in labor requirements. We present computational experiments on problems dealing with up to five work activities and a one-week planning horizon. The results show that the second formulation is stronger in terms of its lower bound and that it is able to find high-quality solutions for all instances with an optimality gap lower than 1%.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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