Comparison of Genetic Operators for the Multiobjective Pickup and Delivery Problem
Bibliographic record
Abstract
The pickup and delivery problem is a pertinent problem in our interconnected world. Being able to move goods and people efficiently can lead to decreases in costs, emissions, and time. In this work, we create a genetic algorithm to solve the multiobjective capacitated pickup and delivery problem, adapting commonly used benchmarks. The objective is to minimize total distance travelled and the number of vehicles utilized. Based on NSGA-II, we explore how different inter-route and intraroute mutations affect the final solution. We introduce 6 inter-route operations and 16 intraroute operations and calculate the hypervolume measured to directly compare their impact. We also introduce two different crossover operators that are specialized for this problem. Our methodology was able to find optimal results in 23% of the instances in the first benchmark and in most other instances, it was able to generate a Pareto front within at most one vehicle and +20% of the best-known distance. With multiple solutions, it allows users to choose the routes that best suit their needs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".