Study on Constructing the Optimization of Omni-Channel Marketing Resource Allocation Based on Dynamic Planning Algorithm in the Era of Digital Transformation
Bibliographic record
Abstract
Reasonable allocation of enterprise marketing resources can ensure that different target markets can be taken into account, but also to ensure that the newly developed markets can be cultivated, so as to maximize the economic benefits of limited resources.The article first combines the principles of marketing resource allocation, constructs a dynamic planning model of marketing resource allocation, and proposes a hybrid genetic algorithm improved by simulated annealing algorithm to solve the marketing resource allocation model.The effectiveness and superiority of the algorithm is tested through simulation and comparison experiments.And take an electrical appliance company as an example, based on the marketing resource allocation model to find the optimal program of the model, to explore the production and sales decision-making that is beneficial to the company.The results can be obtained, with the marketing resource allocation model set in the marketing department of the profit ratio from 10% to 25%, the total profit of the product is increasing, only the pursuit of product sales profit is maximized when the total profit can be obtained is about 17,956,500 yuan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".