Research on solving time-varying vehicle routing with modular operation genetic algorithm based on ALNS
Bibliographic record
Abstract
In the era of rapid growth in online shopping, e-commerce and food delivery have become new shopping methods. This paper focuses on the time-constrained vehicle routing problem in logistics, aiming to minimize the number of vehicles and optimize vehicle dispatch routes as a combinatorial optimization objective. An improved strategy for the traditional genetic algorithm is proposed, which integrates a large neighborhood search algorithm and incorporates modulo operation concepts to enhance the traditional genetic algorithm (ALNS-MGA). In the selection strategy, a combination of elite selection and k-tournament selection is employed to ensure global search capability. During the crossover process, a modulo random linear combination operator (MRLCO) and a multi-path optimal cost operator (MOCO) are introduced for chromosome gene exchange. The former ensures sufficient crossover, while the latter accelerates convergence and enhances the algorithm's local optimization capability. Finally, ALNS is utilized to improve solution quality. The proposed algorithm is tested on the standard Solomon dataset and compared with ALNS-GA, DBO, ALNS algorithms, and the best-known solutions. The experimental results show that the ALNS-MGA algorithm proposed in this paper is closer to the optimal solution than the other compared algorithms, and in some cases, even surpasses the known optimal solutions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".