Supermarket Vegetable Commodities Based on TOPSIS-ARIMA Modeling Optimization Research on Replenishment and Pricing
Bibliographic record
Abstract
With the development of social economy, green and healthy food has gradually become the primary choice of consumers, thus intensifying the competition of vegetable commodities, resulting in the supply of vegetables sometimes exceeds the demand. However, since vegetables are characterized by a short freshness period and deterioration of dish quality, different factors affecting the sales and selling price of the commodities are considered comprehensively to meet the superstore to obtain the maximum return. Firstly, considering that the cost-plus pricing of vegetable commodities has a strong correlation with the discount price, transportation loss rate and storage time, the Topsis model is established to evaluate the different degrees of influence of the above factors, which results in the degrees of influence of the discount price, the transportation loss rate and the storage time on the cost-plus pricing of 21%, 42% and 37%, respectively. Secondly, we calculated the values of the above four indexes and obtained the linear fitting function between the total sales volume and the indexes, and concluded that the discount price is positively related to the total sales volume, with the maximum slope of 9.3218 and the minimum of 0.64; while the cost-plus pricing is negatively correlated with the total sales volume, with the minimum slope of -13.12 and the maximum slope of -0.944, which indicates that when the discount degree is bigger and the cost-plus pricing is lower, each vegetable category will be affected by the discount price and the cost-plus pricing. The lower the discount level and the lower the cost-plus pricing, the higher the sales volume of each vegetable category. Then the autoregressive model (AR) and autoregressive integral sliding average model (ARIMA) are used to fit the maximum value of interest to the sales price and sales volume of cauliflower and aquatic roots and tubers over time in three years to form a training set, and finally the daily replenishment total and pricing of each vegetable category in the coming week are predicted to give advice to the superstores on replenishment and pricing to maximize the revenue of the superstores.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".