Automated Pricing and Replenishment Decisions for Supermarket Fresh Vegetables
Bibliographic record
Abstract
In today's vegetable superstore market, vegetable items have a short shelf life due to their short shelf life. Supermarkets usually replenish the goods on a daily basis based on the historical sales and demand of each item. Therefore, this paper conducts a relevant research on automatic pricing and replenishment decisions for vegetable items based on the measured data of a superstore. First, the trends of different categories under different seasons are plotted. Then, Python linear regression is used to fit the functional relationship equation between sales volume and cost-plus pricing, and an optimization model is constructed with the total daily replenishment as the decision variable and the superstore's revenue as the objective function, so as to derive the predicted sales volume table and pricing strategy table for each category. Finally, the gray prediction model is used to predict and analyze the sales volume of individual items, so as to maximize the superstore's revenue under the premise of trying to meet the market demand for each category of vegetable goods. The model developed in the paper can help superstores predict demand more accurately, make replenishment plans, adjust pricing strategies, and improve market competitiveness.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".