CREATION OF EXPORT-ORIENTED NETWORK OF GRAIN ELEVATORS IN UKRAINE
Bibliographic record
Abstract
Purpose. The scientific paper highlights improving the efficiency of export rail transportation of grain cargoes in Ukraine by introducing shipper routing and concentration of loading at the terminal grain elevators. Methodology. According to the experience of the USA and Canada, one of the most effective ways to reduce costs in the grain to-port supply chain is a shipper routing of the rail traffic. Shipper routing for transportation of grain cargoes involves the concentration of their loading on the multiple junctions. The junctions are proposed to be selected with the use of cluster analysis methods. For the formation of the grain loading concentration areas the authors used methods of set theory and multi-criteria optimization. Findings. Based on agglomerative cluster analysis algorithm, the junctions on a network of Ukrainian railways are selected and the areas of possible concentration of grain loading are formed. DSU-algorithm allowed distinguishing the overlapping and non-overlapping areas of concentration. The problem of selecting non-overlapping areas of the grain loading concentration is formalized as the problem of multiobjective integer programming with boolean variables. The solution of this problem by a modified simplex algorithm allows selecting on the railway network of Ukraine 24 districts of possible grain loading concentration, which cover 70 stations and at minimal additional cost provide routing of about 7.5 million tons of grain per year. Originality. The originality of the work lies in the fact that the authors developed the mathematical procedure for selection of junctions and concentration areas of grain loading at the Ukrainian railway network, taking into account the economic efficiency of the process. Practicalvalue. Application of the developed method of grain loading concentration for the formation of unit trains will significantly reduce the logistics costs in the supply chain of grain to Ukrainian ports for export and, consequently, increase its competitiveness in foreign markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".