An Innovative Modelling and Decision-Support Approach for Evaluating Urban Transshipment Problems Using Electrical Trucks
Bibliographic record
Abstract
As a consequence of urban intensification, logistics planning becomes more important than ever. Electric vehicles have proved to be both environmentally friendly and a lower-cost alternative to internal combustion engine vehicles. However, existing decision methods employed by businesses and municipalities are not universally conducive to the optimization and evaluation of urban transportation systems. An innovative model 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. When faced with decision-making situations that involve multiple and inconsistent performance objectives, it is often preferable to consider several quantifiably good alternatives that provide various, very different perspectives. This paper provides a modeling-to-generate-alternatives (MGA) decision-support procedure that uses the firefly algorithm (FA) metaheuristic for generating sets of maximally different alternatives for electric vehicle planning in urban transshipment problems.
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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".