Inventory Optimization of Fresh Agricultural Products Supply Chain Based on Agricultural Superdocking
Bibliographic record
Abstract
The “agricultural superdocking” mode which has been strong supported by the government has become the main way for fresh agricultural products to enter the market in China. Based on the analysis of fresh agricultural products supply chain inventory management under the “agricultural superdocking” mode, this paper constructs an integrated inventory model for fresh agricultural products of “farmers’ professional cooperatives + distribution centers + supermarkets.” Considering multiple members at each echelon of a supply chain, a model that maximizes the overall profit of the supply chain is proposed. The model assumes that the market demand of fresh agricultural products is affected by freshness and sales prices, and the distribution center is responsible for not only storage, processing, and distribution but also coordinating the production and supply information of farmers’ professional cooperatives and the order sales information of supermarkets. An improved genetic algorithm is developed to solve the nonlinear optimization problem. Results of a case study show that the optimal supply and replenishment strategy under the given supply chain distribution process are obtained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".