Performance Approximation of Emergency Service Systems with Priorities and Partial Backups
Bibliographic record
Abstract
An approximate procedure is presented for priority emergency service systems with partial backups where requests for service with a given origin and priority can be responded to by a certain subset of response units. We introduce and analyze the family of queuing and loss systems with partial service and use them to approximate the distribution of the number of busy response units, which in turn enables us to estimate the average server workloads, immediate and delayed dispatch rates, and waiting delays as system performance measures. We consider systems with zero and infinite queue capacities and let the dispatching policies and service times depend on call priority and customer and server locations. The validity of the approximation model and its computational aspects are studied through numerous tests and a realistic application of locating a fleet of ambulances in downtown Montréal. The online appendix is available at https://doi.org/10.1287/trsc.2017.0810 .
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| 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 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".