Digital Economy Industry and Logistics Industry Convergence Calculation Based on ABC-ACO Algorithm
Bibliographic record
Abstract
As an important part of economic activities, logistics industry ushers in new development opportunities and challenges in the wave of digital transformation. The study explores the path of integration and development of digital economy industry and logistics industry, designs the path of building intelligent logistics ecosystem, and constructs the logistics distribution path optimization model based on time window. When analyzing and solving the logistics distribution path optimization problem, the ant colony algorithm (ACO) is improved by introducing the hierarchical idea of the artificial bee colony algorithm (ABC) and limiting the pheromone concentration on each path, controlling it within a known range, to make up for the shortcomings of the ant colony algorithm of precocious maturity and search stagnation. Using MATLAB software to simulate the logistics and distribution of M fresh food e-commerce enterprises, the comprehensive cost solved based on ABC-ACO algorithm is 75.64 yuan and 33.45 yuan less than the results of ACO and GA solving, respectively, and the optimal route traveling mileage is 21.35 km and 6.03 km shorter than the mileage solved by ACO and GA solving, respectively. It shows that the performance of the improved ant colony algorithm is better than that of the basic ant colony algorithm and the genetic algorithm, and it points out the direction for the future logistics and distribution of the distribution center. The empirical analysis found that the digital economy industry and logistics industry show a synergistic trend, and there is a large space for integration and development.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".