Functional Extension in Python for Simulating Discrete Events in Queueing Networks
Bibliographic record
Abstract
The concept of queueing is ubiquitous in everyday life, whether in supermarkets, banks or amusement parks. The objective of studying queueing networks is to optimize models, either those already implemented or to be implemented, by reducing waiting time and potential financial losses by companies. Discrete event simulation plays a fundamental role complementing the study of queueing models as it can model and simulate real-life scenarios. Therefore, this work proposes the development of a functional extension (library) in Python for discrete event simulations, capable of reproducing and performing performance studies of different models available in the literature, in different computational systems. To validate our proposed solution, we used the SMPL functional extension developed by M. H. MacDougall in C language. Analyzing and comparing the solutions results, we concluded that the developed functional extension in this work is capable of reproducing the same results as a well-established implementation, but using a modern, easy-to-learn, and versatile programming language.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".