Development of the Methodology for Choosing a Rational Carrier of Cargo in International Connection
Bibliographic record
Abstract
One of the effective methods of modeling for decision-making that allows you to take into account most of the properties of the object research is fuzzy logic. The article considers the problem of making decisions in uncertain conditions based on applying the rules of fuzzy logic. Fuzzy set methods are especially useful in the absence of an accurate mathematical model of system operation. Fuzzy set theory makes it possible to apply inaccurate and subjective expertise to a subject area in decision-making without formalizing it in the form of traditional mathematical models. The most influential parameters for the choice of a carrier in an international connection have been determined with the help of expert assessments. The structure and functions of the fuzzy decision-making system regarding the choice of a rational carrier are described. The stages of fuzzy data transformation in the process of logical derivation of solutions are specified. The example of fuzzy inference of the dependence of the parameters of the choice of rational carrier of cargo is given. As a practical implementation of the proposed methodology, a model was created in the MATLAB software environment. According to the results, a rational carrier was determined to transport various goods in international traffic. With the help of MATLAB, graphs of the surface dependence of the carrier’s choice on various influencing factors such as reputation, CMR-insurance, cost of transportation, number of cases of damage and shortage of goods, delivery time were obtained. Implementation in practice shows that the use of research results can reduce the working time of staff and significantly improve the quality of their work in the development and selection of options for delivery of goods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".