On Designing a Fire Emergency Vehicle Fleet
Bibliographic record
Abstract
Problem definition: The configuration of an emergency vehicle fleet (EVF) is critical to ensure that responders have the resources necessary to serve emergencies quickly and help prevent loss of life and property. Determining the fleet’s optimal design involves decisions regarding its size, the spatial distribution of the stations, and the extent of collaboration among them. We study the optimal design of a fire service fleet that is characterized by low utilization of the vehicles. The primary tradeoff is between the cost of having too many vehicles in the fleet and the cost incurred by not serving a fire in a timely manner. Methodology/results: EVFs are highly expensive systems for the public sector. We introduce a novel queueing-model approach tailored to the EVF as a light-traffic demand system. Our model incorporates the necessary performance measures, that is, the response times and the fleet capacity, for determining the optimal fleet configuration. We validate the model and demonstrate its adaptability via an application to the Toronto Fire Services. Managerial implications: Adopting our proposed model can assist managers in making informed strategic decisions regarding the effective design of the EVF. The methodology can be used to determine the most efficient strategy for investment in fire services in a large metropolitan region. Funding: This work was supported by three Discovery Grants from Natural Sciences and Engineering Research Council (NSERC) to the last three authors. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0357 .
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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 | 0.007 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".