Multi-objective electric bus scheduling problem considering multi-vehicle types.
Bibliographic record
Abstract
The electrification of public transportation, particularly the transition from diesel/gas to electric bus fleets, poses a unique set of challenges related to efficient scheduling and operational management. The Electric Bus Scheduling Problem (EBSP) is a critical aspect of this transition, as it involves optimizing the assignment of electric buses to predetermined timetable trips to minimize fleet size and operational costs. This paper presents a systematic approach to address the multi-depot and multi-vehicle type electric bus scheduling problem (MD-MVT-EBSP) within the complex framework of urban transportation systems. The problem is tackled by developing an optimization model, which, alongside the genetic algorithm, results in finding the optimal schedule and recharging trips while the total cost of using an electric bus fleet is minimized. The proposed method not only achieves the optimal schedule but also addresses the crucial aspects of determining the required number of each vehicle type and the associated charging specifications needed to fulfill the timetable trips. A rigorous investigation of a case study is performed by employing a real-world transit network dataset from Canadian cities.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".