A Matheuristic for the Electric Vehicle Routing Problem with Capacitated Charging Stations
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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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".