A Multicriteria, Bat Algorithm Approach for Computing the Range Limited Routing Problem for Electric Trucks
Bibliographic record
Abstract
As a result of increasing urban intensification, civic planners have devoted additional resources to more sustainability-focused logistics planning. Electric vehicles have proved to be both a lower cost alternative and more environmentally friendly than the more ubiquitous internal combustion engine vehicles. However, the predominant decision-making approaches employed by businesses and municipalities are not necessarily computationally conducive for the optimization and evaluation of urban transportation systems involving electric vehicles. An innovative modelling and planning approach is proposed to enable urban planners to more readily evaluate the contribution of electric vehicles in city logistics and to support the decision-making process. Specifically, this paper provides a multicriteria modelling-to-generate-alternatives (MGA) decision-support procedure that employs the Bat Algorithm (BA) metaheuristic for generating sets of alternatives for electric vehicle planning in urban transshipment problems. The efficacy of this multicriteria, BA-driven MGA approach for creating planning alternatives is demonstrated on an urban transshipment problem involving electric trucks.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".