Optimal Electric Bus Charging Scheduling with Multiple Vehicle and Charger Types Considering Compatibility
Bibliographic record
Abstract
Battery electric buses (BEBs) are a sustainable and environmentally friendly solution for modern transit systems, offering zero tailpipe emissions and reduced noise pollution. However, due to the relatively short driving range and limited charging resources, it is crucial to jointly optimize the BEB fleet, charging schedules, and charging infrastructure to improve operational efficiency. This study proposes a BEB charging scheduling method with multiple types of vehicles and chargers. In particular, a partial charging strategy, charging continuity, and the compatibility of vehicles with chargers are incorporated. We first formulate a mixed-integer programming model to minimize the total costs of the BEB transit system, including the purchase costs of chargers, fleet composition costs, and electricity costs. Then, a column generation (CG) algorithm is designed to solve the model, and a case study based on a real transit network in Nanjing, China, was conducted. The results verify the effectiveness of the proposed model and algorithm. The findings in this study provide practical guidance on BEB charging scheduling and promote the sustainable development of bus transit systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".