Co-optimization Research on Digitalization of Enterprise Human Resource Management and Integrated Construction of Measurement and Training Based on Optimization Algorithm
Bibliographic record
Abstract
In this paper, we construct a multi-level network based on corporate mobility relationships to quantify human resource attributes.The cuckoo search algorithm (CS) is chosen to enhance the global optimization capability of human resource management scheme.Combine CS and XGBoost to construct CS-XGBoost algorithm, and realize the optimal solution of HRM scheme through hyperparameter optimization and other steps.The multi-project human resource management of construction enterprises is taken as an example to verify the auxiliary value of CS-XGBoost algorithm in the generation of optimal management scheduling scheme.Empirical studies show that the algorithm can obtain the optimal solution in about 450 iterations.In multi-project scheduling management, the optimal duration can be reduced to 510 days, which is better than the comparison algorithm.With the introduction of demand prioritization requirements, the algorithm can effectively balance the differences in project duration, project cost and employee working time.The CS-XGBoost algorithm can be used to quickly realize the optimal decision-making of enterprise human resource scheduling management, save costs and improve efficiency.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".