A matheuristic algorithm to solve the electric vehicle routing problem with capacitated charging stations
Bibliographic record
Abstract
In this research we focus on an electric vehicle routing problem in which 1) vehicles can be partially recharged between customer visits, 2) the quantity of energy recharged is a piecewise linear function of the time spent charging and of the current state of charge of the vehicle, and 3) the number of vehicles simultaneously charging at each charging station does not exceed the number of available chargers. The objective is to minimize the total time needed to serve all the customers. In this problem, resource synchronization takes place at nodes whose visit is a decision that depends on the sequence of customers served on the routes and on the objective. We propose two mixed integer linear programming formulations of the problem based either on charging stations replication and recharging paths. Solving the model using a commercial solver allows us to solve only small-sized instances. To tackle large-sized instances, we propose an hybrid algorithm that combines two components: an iterated local search algorithm responsible for generating a pool of high-quality routes and a branch-and-cut algorithm responsible for assembling a solution to the problem from the generated pool. We perform a comparison between four assembling strategies. Our computational results show that the strategies have different degrees of effectiveness and efficiency in addressing the capacity constraints of charging stations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".