Research on Multidimensional Computational Optimization of Enterprise Organizational Structure Supported by PSACO Algorithm in the Era of Digital Economy
Bibliographic record
Abstract
The purpose of this paper is to study the multidimensional computational optimization problem of enterprise organization structure.Based on the value dimension, this paper designs a kind of enterprise organizational structure which contains six dimensions such as demand de inition, sales and so on.And the optimization model of enterprise organizational structure is studied by taking a demand-pull enterprise as an example.In the iterative process of the algorithm, the particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm are run alternately to form the PSACO algorithm to solve the optimization model of enterprise organizational structure constructed in this paper.The experimental results show that the PSACO algorithm used in this paper can maintain stable and good convergence performance under different demand sizes.The enterprise organizational structure optimization model and solving algorithm in this paper can achieve ideal resource allocation scheme and effect, and can still achieve good solving effect in large-scale enterprise organizational structure optimization adjustment.It provides good decision support for the optimization and improvement of enterprise organizational structure in the era of digital economy, and has important application value.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".