Binjai Train Ticket Counter Queue Simulation Using Weibull Service Distribution
Bibliographic record
Abstract
This research aims to improve the efficiency of train ticket counter services at Binjai Station through the use of Weibull service distribution-based queuing simulations. Long queues and excessive waiting times are often problems at many train stations, and this research aims to address these problems.This study collects queuing data from train ticket booths at Binjai Station over a certain period and analyzes them to identify existing queuing patterns. The Weibull service distribution was chosen as the appropriate model to describe the ticket counter service time, because this distribution has the flexibility to handle variations in service time well. Queue simulation is carried out using simulation software that models the queuing process at the train ticket counter. Weibull distribution parameters are integrated into the simulation to predict service time at the ticket counter. In this simulation, various scenarios and strategies for improving service efficiency are evaluated to identify the best alternative that can be implemented at Binjai Station. The results of this study will provide guidance to the management of the Binjai train station in making decisions regarding increasing the efficiency of ticket counter services. By optimizing service time and reducing customer waiting time, it is expected to increase customer satisfaction and operational efficiency of train stations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".