Logistics Distribution Path Optimization Considering Carbon Emissions and Multifuel‐Type Vehicles
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
With the development of a sustainable economy, higher requirements are put forward for logistics enterprises, which not only need to meet the requirements of profit growth but also to meet the need of sustainable development. A vehicle routing problem (VRP) optimization model considering carbon emissions and multifuel‐type vehicles (VRP‐CEMF) is proposed to solve the problems of air pollution and high transportation cost in the current logistics distribution. An improved genetic algorithm (IGA) is designed to solve the VRP‐CEMF. The impact of carbon emissions and multifuel‐type vehicles on the logistics distribution path is explored by a real example simulation. The results show that the logistics distribution path optimization considering carbon emissions and multifuel‐type vehicles including hybrid electric vehicles and hydrogen‐fueled vehicles can significantly reduce carbon emissions on the premise of ensuring the lowest total cost. Furthermore, the impact of carbon emissions, hydrogen fuel price, and customer demand on the logistics distribution path is discussed by sensitivity analysis. The research results of this paper provide an effective reference for enterprises to control carbon emissions in the process of logistics distribution and promote the green transformation of logistics.
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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 | 0.000 | 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 it