Sustainable planning battery electric buses charging station under two decision-making criteria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it