Maintenance and repair: application of simulation and mean value analysis to a repair facility model for finding optimal staffing levels
Bibliographic record
Abstract
Staffing problems arise in a wide range of applications including job shops, call centres, and hospital emergency departments. They are characterised by the need to allocate shift workers with varying skills to handle an arrival stream of tasks having different sub-task routings and (sub-task) skill requirements. The Manitoba Telecom Service Trouble Diagnosis and Repair System (TDRS) has 3 skill-levels of staff handling multiple types of faults occurring in telephone switching equipment. TDRS is a pure staffing problem having no equipment constraints: the only resource constraint is staff itself. The object of this study is to show how this can be modelled as an open network of queues with feedback and allowing for temporal and faultclass heterogeneity. Analytic mean value analysis then facilitates validation and selecting feasible staffing strategies for closer examination by simulation. The purpose of experiments using simulation is to find effective performance visualisations and optimal staffing allocations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".