Solving the Vehicle Routing Problem via Distance-Aware Clustering and Simulated Annealing
Bibliographic record
Abstract
The problem of routing a given number of vehicles leaving a depot to serve customers is known as the Vehicle Routing Problem (VRP).VRP is used in various fields such as logistics, supply chain and distribution.To solve VRP, in this study, we propose a solution which uses a heuristic algorithm that we developed to distribute customers to vehicles and then optimizes the route of each vehicle using Simulated Annealing technique.Our solution aims to solve VRP by generating routes of similar length for each vehicle in a short enough time to be used in real-time applications when no capacity value is given for the vehicles.To measure the performance of our solution, we compared it with OR-Tools, an open source VRP library, using problem instances that we have created by generating synthetic data.We found that in most cases it performed better and was able to create shorter routes.Thus, we consider it as an effective and performant solution for classical VRP.Since we offer a direction-oriented solution, we think that it produces useful routes in reallife problems, especially in distribution-based real-life problems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".