APPROACH TO THE FORMING OF RATIONAL TECHNOLOGY FOR THE EXPORT CARGOES DELIVERY IN SUPPLY CHAIN ON THE PRINCIPLES OF CO-MODALITY
Bibliographic record
Abstract
In this article, we propose an approach to the forming of rational technology for the export cargoes delivery in the supply chain on the principles of co-modality in the context of effective interaction of delivery participants, which allows reducing the costs of all them through the optimal use of resources related to delivery and increasing the competitive advantages of products. Taking into account the strengthening of cooperation between road and rail transport, under modern conditions, combined transport within the international transport corridor is proposed as an alternative option to promote cargo flow in the supply chain. As a result of experimental studies carried out on the basis of the simulation model of the delivery process in the Petri Nets, the time characteristics of technological processes under alternative schemes depending on the input parameters of the model have been determined. A comparative assessment of alternative delivery schemes based on mathematical models has made it possible to establish that the rational scheme for the delivery of export cargo from Ukraine to Germany via the Pan-European Corridor III is the Rolling Highway technology, which ensures compliance with the cargo owner's requirements for delivery time and cost established by the contract. The proposed approach is recommended as a tool for making managerial decisions when planning and organising the delivery of export cargoes from Ukraine to the EU countries in order to effectively manage supply chains by minimising the cost of delivery and environmental damage.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".