An Elementary Approach to the Vehicle Routing Problem via Python and Google API
Bibliographic record
Abstract
Commercial organisations commonly use operational research tools to solve vehicle routing problems. This practice is less commonplace in charity and voluntary organisations. In this paper, we provide an elementary approach for solving the Vehicle Routing Problem (VRP) that we believe can be easily implemented in these types of organisations. The proposed model leverages mixed integer linear programming to optimize the pickup sequence of all customers, each with distinct time windows and locations, transporting them to a final destination using a fleet of vehicles. To ensure ease of implementation, the model utilises Python, a user-friendly programming language, and integrates with the Google Maps API, which simplifies data input by eliminating the need for manual entry of travel times between locations. Troubleshooting methods are incorporated into the model design to ensure easy debugging of the model’s infeasibilities. Additionally, a computation time analysis is conducted to evaluate the efficiency of the code. A node partitioning approach is also discussed, which aims to reduce computational times, especially when handling larger datasets, ensuring this model is realistic and practical for real-world application. By implementing this optimized routing strategy, logistics companies or organisations can expect significant improvements in their day-to-day operations, with minimal computational cost or need for specialised expertise. This includes reduced travel times, minimized fuel consumption, and thus lower operational costs, while ensuring punctuality and meeting the demands of all passengers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".