Research on the multi-objective operation efficiency improvement path of enterprises based on reinforcement learning algorithm
Bibliographic record
Abstract
In enterprise operations, multi-objective optimization involves multiple conflicting objectives such as cost escalation control, customer satisfaction, and production efficiency.Based on reinforcement learning algorithm, the article deals with multi-objective optimization problem in enterprise operation through the interactive learning between intelligent body and environment, for which a multi-objective operation efficiency improvement path for enterprise based on Q-learning scheduling is designed.The simulation data is utilized to generate the PDR tree structure, and subsequently, the intelligent body is prompted to complete the multi-objective operation learning of the enterprise through several iterations.On this basis, the intelligent body completes all the actions and generates scheduling strategies to improve operational efficiency.The model proposed in this paper can predict the demand changes of enterprises in the future time window and make the best decision to improve the operational efficiency.Under the model of this paper, the mean values of pure technical efficiency as well as scale efficiency of 10 firms in 2024 are 0.9 and 0.933, respectively, and they are predicted to continue to grow in 2025.The model reduces the firms' average operating costs and administrative expenses, while employee compensation and fixed assets increase by 49.58% and 19.48%.Since the survey period, the TFP index of all 10 companies is greater than 1, which indicates that, the application of the model in this paper improves the operational efficiency of the companies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".