A Novel Green Logistics Vehicle Scheduling Method Against Road Congestion Utilizing Vehicle–Road–Cloud Collaborative Technology
Bibliographic record
Abstract
In modern urban logistics and schedule systems, road congestion stands out as a primary contributor to heightened energy consumption in new energy logistics vehicles. Addressing this issue, this study establishes a scheduling method for new energy logistics vehicles comprising several key components: Using the vehicle–road–cloud collaborative technology, the number of vehicles on the road is obtained, and the road congestion coefficient is calculated by combining the speed‐flow model, and then the nonlinear energy consumption model for new energy logistics vehicles is studied. Additionally, a VRC‐GVRP model is developed considering multiple constraints with the aim of minimizing total energy consumption. To solve this model, an initial solution is constructed using an energy‐saving algorithm, while exploring a Cauchy variational strategy and a parallel local search to propose an improved adaptive large neighborhood search (ALNS) algorithm. An illustrative analysis is conducted within an industrial park, based on the real‐time traffic information aggregated to the cloud control platform, and the scheduling problem of new energy logistics vehicles is solved. The experimental results indicate that the enhanced ALNS algorithm exhibits rapid convergence and yields high‐quality solutions. Compared to the situation without vehicle–road–cloud collaboration technology, despite the increase in the total distance traveled by new energy logistics vehicles, the proposed method effectively reduces total drive time and total energy consumption. As the congestion factor increases, the percentage of reduction in total time and total energy consumption becomes higher and higher, indicating that this method is of great significance for improving the work efficiency of new energy logistics vehicles and achieving energy conservation and emission reduction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".