European Driver Rules in Vehicle Routing with Time Windows
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As of April 2007, the European Union has new regulations concerning driver working hours. These rules force the placement of breaks and rests into vehicle routes when consecutive driving or working time exceeds certain limits. This paper proposes a large neighborhood search method for the vehicle routing problem with time windows and driver regulations. In this method, neighborhoods are explored using a column generation heuristic that relies on a tabu search algorithm for generating new columns (routes). Checking route feasibility after inserting a customer into a route in the tabu search algorithm is not an easy task. To do so, we model all feasibility rules as resource constraints, develop a label-setting algorithm to perform this check, and show how it can be used efficiently to validate multiple customer insertions into a given existing route. We test the overall solution method on modified Solomon instances and report computational results that clearly show the efficiency of our method compared to two other existing heuristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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