Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions
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
A Multi-Depot Green Vehicle Routing Problem (MDGVRP) is considered in this paper. In MDGVRP, Alternative Fuel-powered Vehicles (AFVs) start from different depots, serve customers, and, at the end, return to the original depots. The limited fuel tank capacity of AFVs forces them to visit Alternative Fuel Stations (AFS) for refueling. The objective is to minimize the total carbon emissions. A Two-stage Ant Colony System (TSACS) is proposed to find a feasible and acceptable solution for this NP-hard (Non-deterministic polynomial-time) optimization problem. The distinct characteristic of the proposed TSACS is the use of two distinct types of ants for two different purposes. The first type of ant is used to assign customers to depots, while the second type of ant is used to find the routes. The solution for the MDGVRP is useful and beneficial for companies that employ AFVs to deal with the various inconveniences brought by the limited number of AFSs. The numerical experiments confirm the effectiveness of the proposed algorithms in this research.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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