The Optimization of Path Planning for Express Delivery Based on Clone Adaptive Ant Colony Optimization
Bibliographic record
Abstract
In recent years, China's express delivery market has developed rapidly in the context of a booming economy. However, logistics costs are still high, which will affect the decision-making and policy making of relevant departments. Therefore, it is essential to optimize the last-mile assignment problem (LMAP) to meet the consumer’s demand for delivery time and reduce economic expenditure. The LMAP of express delivery requires multiple packages to be delivered to different destinations. Finding the path with the minimum delivery cost and time is an NP-hard problem, and it is impossible to obtain the optimal solution by enumerating all possible answers. This study proposes a new express delivery path planning method based on a clone adaptive ant colony optimization (CAACO) to find suboptimal solutions. Moreover, a new distribution cost fitness function constructed by weighing the economic expenditure and time of express delivery is designed. Specifically, a new adaptive operator and a novel clone operator are also designed to accelerate the speed of convergence. Finally, by comparing the performance of CAACO with ant colony optimization (ACO), simulated annealing (SA), and genetic algorithm (GA), the effectiveness of CAACO in solving the express LAMP is verified. In the simulation results, it is obvious that the economic expenditure and time of express delivery based on the CAACO are lower than ACO, SA, and GA, and the convergence speed is also faster than the SA and GA. It can be seen that CAACO has valuable benefits in solving LMAP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".