Simulation Model of Multiple Queueing Parameters: A Case of Vehicle Maintenance System
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.
Bibliographic record
Abstract
Often, taking strategic decisions in maintenance systems are very difficult and at times impossible, because the data required are either not available or not in the right format. This study has developed a computer queueing model with an integrated set of algorithms for simulation of multiple queueing parameters. It used ITC workshop in Nigeria as a case study, which has a single channel multi-server queueing system. The developed simulation model was validated with the standard mathematical model results and found to be reliable. The structure of the software package is flexible and robust enough to accommodate any value of maximum simulation time and number of crew size as the storage capacity of the computer allows. In addition to being user friendly, performing an experiment using the simulation model is more than forty million times faster than doing it with the case study. It is also equipped with post object oriented animation and digital tracing for each discrete step of the simulation run. Hence, it is an effective queueing model pedagogical tool. With the simulation model, decision taking is made easier, requiring less data and as fast and the simulation runs.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it