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Record W4389145753 · doi:10.36910/automash.v2i21.1204

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

2023· article· en· W4389145753 on OpenAlex
Yevhen Kush, Дар’я МУКОВСЬКА

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueСУЧАСНІ ТЕХНОЛОГІЇ В МАШИНОБУДУВАННІ ТА ТРАНСПОРТІ · 2023
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsTransport Canada
Fundersnot available
KeywordsQueueing theorySortingTruckProcess (computing)Duration (music)Raw materialQueuePoint (geometry)EngineeringComputer scienceSimulationOperations researchAutomotive engineeringAlgorithmMathematics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.497
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.267
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it