Sustainable planning battery electric buses charging station under two decision-making criteria
Bibliographic record
Abstract
This study addresses the sustainable planning of charging locations and times for battery electric buses (BEBs) under uncertain weather conditions, aiming to minimize the operational risks and enhance the environmental sustainability. With BEBs as a key component of sustainable urban development, their operational efficiency and environmental impact are heavily influenced by uncertain weather conditions. To model this situation, we introduce a new risk measure, excess probability, to quantify the impact of weather uncertainty on BEB operations. To address the inherent uncertainties in weather conditions, three globalized robust optimization (GRO) models are built for our studied problem, which can be reformulated as mixed-integer linear programming (MILP) models. A new tailored Benders decomposition (BD) algorithm is designed for MILP models with acceleration strategies. The advantages of the proposed method are verified via a real case about a bus route in Edmonton. The results also highlight the importance of addressing risk preferences in decision-making process and balancing the operational costs with service reliability.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".