Solving the Vehicle Routing Problem via Distance-Aware Clustering and Simulated Annealing
Why this work is in the frame
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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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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 | 0.001 |
| 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