Modeling horizontal collaboration efficiency of several supply chains
Bibliographic record
Abstract
Research background: Economic relations, which are formed under conditions of uncertainty and instability of the surrounding, require highly efficient techniques of engineering logistics activities. The high competitiveness on the logistics market forced to search new technologies that would give advantages over competitors. One of these advantages could be the decreasing of the transport service cost. The logistics activity, with the collaborative management of the complex of transport and logistics systems, is not well understood. The issues of the effectiveness of using various technologies in servicing a complex of logistics systems have not been sufficiently addressed. The effectiveness of horizontal collaboration of individual participants of various logistics systems to achieve common goals has not been fully studied. Lack of an unequivocal scientific justification and relevant practical developments determined the choice of the research topic and its relevance. Purpose of the article: The aim of the paper is to identify patterns of influence of horizontal collaboration on efficiency of several supply chains. Methods: The article used modelling of transportation process without horizontal collaboration – traditional one and with it. Invest indicators for assess collaborative and separated effectiveness of several supply chains were used. Findings & Value added: The use of horizontal collaboration technology leads to a synergistic effect: reducing the total number of vehicles required to service several supply chains; increased performance indicators. The patterns of the influence of technological parameters of the transport process on the effectiveness of horizontal transport collaboration are revealed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".