A Matheuristic for the Electric Vehicle Routing Problem with Capacitated Charging Stations
Why this work is in the frame
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Bibliographic record
Abstract
Existing research on Electric vehicle routing problems (E-VRPs) assumes that charging stations (CSs) can simultaneously charge an unlimited number of electric vehicles. In practice, however, CSs have a limited number of chargers. In this research, we investigate the impact of considering these capacity restrictions. We focus on the electric vehicle routing problem with nonlinear charging function (E-VRP-NL). We first extend existing mixed integer linear programming formulations of the E-VRP-NL to deal with capacitated CSs. We then present a route-first assemble-second matheuristic to tackle the problem. In the first stage of this method, we rely on an existing metaheuristic to generate a pool of high-quality routes while relaxing the capacity constraints. In the second stage, we use a Benders' like decomposition to assemble a solution to the problem by assembling routes from the pool. We evaluate four different assembling strategies. The results suggest that our algorithm performs well on a set of instances adapted from the literature.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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