A Vehicle Routing Problem with Consideration of Green Transportation
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 aims to investigate the Green Vehicle Routing Problem (GVRP), which considers stochastic traffic speeds, so that fuel consumption and emissions can be reduced. Considering a heterogeneous fleet, the fuel consumption rate differs due to several factors, such as vehicle types and conditions, travel speeds, roadway gradients, and payloads. A mathematical model was proposed to deal with the GVRP, and its objective is to minimize the sum of the fixed costs and the expected fuel consumption costs. A customized genetic algorithm was proposed for solving the model. The computational experiments confirm the efficiency of the algorithm and show that the solution of GVRP is quite different from that of the traditional vehicle routing problem. We suggest that a company should use light vehicles to service the customers situated at higher terrains. The customers with higher demands can be visited earlier, but the customers situated at higher terrains or far away from the depot should be visited later. The study also found that the fixed costs of dispatching vehicles are critical in GVRP; a logistics company may thus tend to use large vehicles, despite that it may cause higher fuel consumption and emissions. The proposed model and algorithm are capable of suggesting a guidance for green logistics service providers to adopt a beneficial vehicle routing plan so as to eventually achieve a low economic and environmental cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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