Optimizing a Capacitated Vehicle Routing Problem with Scheduled Arrival, Split Deliveries within Time Windows and Emission Consideration
Bibliographic record
Abstract
The Capacitated Vehicle Routing Problem (CVRP) has gained significant attention in both academic and industrial circles due to its pivotal role in optimizing logistic systems. In the context of evolving distributor companies and the growing integration of logistics with broader societal concerns such as climate considerations, this paper delves into a CVRP variant that includes time windows and split deliveries. Real-world assumptions are incorporated to enhance the practical applicability of the study. A mathematical model is proposed to minimize both economic costs and pollutant emissions. Given the unavailability of cost information for all possible routes, a cost function is estimated through multiple linear regression, considering both distance and time factors simultaneously, in order to associate to each link costs and emissions. To validate the effectiveness of the proposed model, a real-world case study involving an industrial distribution company is investigated. The results demonstrate a significant improvement compared to the company's current operational procedures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".