Cooperative Electric Vehicles Planning
Bibliographic record
Abstract
This paper introduces the Cooperative Electric Vehicles Planning Problem (CEVPP), which consists in finding a path for each vehicle of a fleet of electric vehicles, such that the global plan execution time (including travel time, charging time and waiting time) is minimal (e.g., by limiting the number of vehicles who need to charge simultaneously at the same charging station, which leads to waiting time). We show that the strategy which consists in planning each possible permutation of EVs and keeping the one providing the best solution is not only time intractable, but also not optimal. We propose different centralized planning algorithms to solve CEVPP instances: (1) a baseline non-cooperative CEVPP planner, (2) an optimal cooperative planner that finds a solution inside a carefully designed state space, and (3) multiple variants of an approximate cooperative planner based on the Cooperative-A* algorithm. We compare the solutions' quality and computation times obtained by these CEVPP planners. Our empirical results show that our best approximate cooperative EV planner found solutions with a reasonably small computational overhead compared to the baseline algorithm. The solutions found by our cooperative planners had significantly lower plan execution time globally, including travel time, waiting time and charging time, than the solution found by our baseline non-cooperative planner. On average, our empirical results show that our cooperative algorithms decreased the global (including each EVs) waiting time by more than 90%, while having a negligible impact on the charging and driving time.
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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 | 0.000 | 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) | 0.007 | 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".