Research on Cost and Risk Co-minimization Model of Cross-border Logistics in Guangxi Based on Monte Carlo Approach
Bibliographic record
Abstract
Cross-border e-commerce refers to a kind of international commercial activity in which trading entities belonging to different customs borders reach transactions, make payments and settlements through e-commerce platforms, and deliver commodities through cross-border logistics to complete the transactions. Based on the relevant components of cross-border logistics cost and risk, this paper applies Markowitz, Capital Asset Pricing Model (CAPM), Value at Risk Model (VaRM), and Creditmetrics model to measure the risk of cross-border logistics, respectively. Through the cost measurement of cross-border logistics losses, a simplified logistics risk cost minimization model is derived. The model is applied to Guangxi’s cross-border logistics company M. Monte Carlo simulation is used to estimate the risk and cost of its cross-border logistics, respectively, and the probability of IRR>13.246% is simulated to be 32.963%, which indicates that the probability of cross-border logistics results exceeding the IRR of 13.246% given in the economic analysis is 32.963%. It can be seen in the logistics cost estimation that the mean value of the monthly logistics cost estimation of Company M is 2764000.564 yuan, and the standard deviation is 15126.36321 yuan, and after 3000 simulation operations, the logistics cost estimation has a 95% probability of falling on the interval [2572000 yuan, 2964000 yuan]. In response to the results of the simulation operations, a logistics risk and cost control strategy is proposed that is consistent with the long-term development of M Company.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".