Joint Optimization of Electric Bus Scheduling and Fast Charging Infrastructure Location Planning
Bibliographic record
Abstract
Transit authorities are transitioning from conventional buses to electric buses (EBs) due to growing concerns about air quality and greenhouse gas emissions. Many mathematical optimization models have been developed for scheduling conventional buses. However, such models would not fit EBs due to their limited travelling range and long charging time. Such operational differences have prompted new research into the literature on the charging station location problem. This study combines EB scheduling with fast-charging infrastructure location planning to minimize total scheduling and charger installation costs. We propose an Integer Linear Programming (ILP) formulation for a path-based model, solved using four developed branch-and-price algorithms, and test their performance across various instances. Our computational experiments show which branching strategy is computationally efficient in terms of execution time and optimality gap. Finally, we conduct a real case study and perform a sensitivity analysis to identify the most cost-effective type of electric bus, considering the specific characteristics of different EB types.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".