Research on Cost Optimization of Fresh Commodities: An E-commerce Business Perspective
Bibliographic record
Abstract
With the high-quality economic development and per capita income of China improving significantly, the demand for fresh commodities surges unprecedentedly. This paper outlines two E-commerce channels for selling fresh goods and establishes an inventory routing problem model. Following this, two sub-problems are discussed, namely the transportation cost of the direct distribution channel as well as the warehousing cost and the deterioration cost of the channel involving offline distributors. To optimize transportation costs, a capacitated vehicle routing problem model is built, and the ant colony algorithm is improved by considering the load factor of distribution vehicles. The optimal route from a single distribution center to 50 customer sites is obtained by solving the specific example using MATLAB. To find the optimal solution for the warehousing cost and the deterioration cost, distribution periods are interpreted as piecewise functions. With one of the periods being selected, the summation of the aforementioned costs is obtained by setting parameter values. The inventory routing model and the improved ant colony algorithm proposed in this paper fully consider the actual selling conditions of fresh food e-commerce businesses, which results in more accurate analysis, thus providing a rational decision-making basis for the e-commerce businesses to analyze economic benefits.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| 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".