Intelligent Loading of Scattered Cargoes Based on Improved Ant Colony Optimization
Bibliographic record
Abstract
This paper improves the ant colony optimization (ACO) to optimize the scattered cargo loading problem. Firstly, the concept of scattered cargoes was defined clearly, and a mathematical model was established to maximize the volume utilization under multiple constraints of scattered cargoes. Next, the wall-based loading strategy was put forward to rationalize the spatial arrangement and stabilize the loaded cargoes. After that, the ACO's expectation function was modified to ensure the consistency between cargo selection and the said strategy. In addition, a pheromone heuristic factor and an expected heuristic factor, both of which are dynamically adjustable, were set up to enhance the global search ability of the proposed algorithm, wall-based ACO (WBACO). Finally, three experiments were conducted respectively on classical weakly heterogeneous data, actual production data with weak heterogeneity, and classical strongly heterogeneous data, to verify the performance of our algorithm. In Experiment 1, the WBACO achieved an objective function value 2.6 % higher than the B&R algorithm and 3.1 % higher than the CBGAT. In Experiment 2, the WBACO led the space-based ACO by 6.82 % in average volume utilization and 3.35 % in optimal volume utilization. In Experiment 3, the result of the WBACO was 0.91 % smaller than the B&R algorithm on wtpack7_51, and 6.97 % greater than the latter on wtpack7_74. The experimental results show that the WBACO lays theoretical and practical bases for intelligent loading of scattered cargoes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".