On Designing a Fire Emergency Vehicle Fleet
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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 it