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 fault-class 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 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 | 0.001 |
| 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".