Solving jointly districting and resource location and allocation problems: An application to the design of Emergency Medical Services
Bibliographic record
Abstract
This paper proposes an integrated approach to jointly tackle districting and resource allocation decisions, two interrelated problems that, in most cases, are handled separately. The proposed approach is applied to the context of Emergency Medical Services (EMS), where a large territory needs to be covered by a limited number of resources, i.e., the ambulances. The territory is usually split into districts; each district receives a share of ambulances, which are managed quasi-independently. This paper focuses on the importance of districting decisions, which will impact daily operations and, therefore, the performance of the system. To address the districting and resource location and allocation problems jointly, it proposes an iterative algorithm that exploits the interaction between the strategic (i.e., the districting) and the operational (i.e., the location and allocation of resources) decisions to build compact and balanced districts and, at the same time, find the location and allocation of resources that maximize the performance in terms of system’s response time. Starting from an initial set of districts, the iterative algorithm solves the associated resource location and allocation problem for each of them. Then, according to the performance reached by the location–allocation solutions, the districts are modified. Applied to realistic instances inspired by the city of Montreal, Canada, the algorithm produced results that improved simultaneously the system’s expected response time and the metrics assessing the quality of the districts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".