Algorithm for a simulation model for the selection of a rational type of vans on technological routes of the transport and recycling system for recycling of a metallurgical enterprise
Bibliographic record
Abstract
The article presents an algorithm of the simulation model for selecting the rational type of trucks on the technological routes of the transport and production system of waste transportation in the conditions of a metallurgical enterprise. The study was conducted on the basis of formalized models and queuing systems theory. In the simulation, a discrete-event model of a closed queuing system was chosen as the basic model. In this model, cars are represented as requests that go through the following phases of service: loading at the temporary waste storage dump; movement to the unloading point (crushing and sorting complex); unloading raw materials to the crushing and sorting complex; returning to the loading point along the same route; servicing of vehicle breakdowns. The following data were used to build the simulation model through statistical studies of the transportation process on technological routes: vehicle loading time, vehicle unloading time, and time determined by the duration of vehicle failure. When constructing the algorithm of the simulation model, the duration of one experiment and the number of simulation experiments were determined using the methods of mathematical statistics and the theory of experiment planning. The modeling process begins with the input of the following initial data: the number of vehicle units, the average vehicle travel time, the current amount of raw materials in the crushing and sorting complex, the vehicle carrying capacity, the intensity of raw material supply to the crushing and sorting complex, the vehicle unloading time, the intensity of minor vehicle breakdowns, and the intensity of vehicle repair. The algorithm is built in accordance with the principles of special states and meets the requirements for discrete-event models. As a result of the algorithm development, it will be possible to determine the required number (types) of vehicles necessary to ensure the smooth functioning of this transport and production system, to determine the idle time coefficient and the coefficient of time losses due to technological delays of vehicles. Key words: modeling, simulation model, dump truck, carrying capacity, metallurgical slag, crushing and screening equipment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".