ORAHS2024
Abstract Submission

36. Ambulance stochastic optimization for EMS: a hierarchical compromise model and a matheuristic algorithm.

Contributed abstract in session TD-5: Ambulance Management, stream Regular talks.

Tuesday, 16:00-17:30
Room: Room S6

Authors (first author is the speaker)

1. Imanol Gago-Carro
BCAM - Basque Center for Applied Mathematics
2. María Merino
Mathematics, University of the Basque Country-UPV/EHU; Basque Center for Applied Mathematics-BCAM
3. Unai Aldasoro
Applied Mathematics, University of the Basque Country (UPV/EHU)
4. Dae-Jin Lee
IE University

Abstract

Health emergencies represent critical junctures where precise decision-making can determine between a dire outcome and a manageable situation. The efficiency of emergency medical service (EMS) operations hinges, to a large extent, on strategic ambulance positioning and real-time allocation. This study delves into the challenges of ambulance location-allocation within the geographical area of the Basque Country (Spain), served by a fleet of 90 ambulances composed of both ALS and BLS ambulances.

Leveraging historical data, we use a Box-Cox Cole and Green distribution to forecast response times. Our approach centers on a two-stage stochastic mixed 0-1 linear programming model aimed at optimizing the primary objective of maximizing expected coverage. Additionally, we account for secondary objectives, such as minimizing average response time and integrating risk aversion measures like Conditional-Value-at-Risk, within a hierarchical compromise framework.

Acknowledging the computational complexities inherent in our model, we introduce a novel matheuristic algorithm. This algorithm, employing primal decomposition, demonstrates promising efficiency in addressing the intricate EMS optimization problem across medium and large-scale instances. Our research enhances EMS effectiveness and responsiveness, ultimately improving patient care outcomes during critical emergencies.

Keywords

Status: accepted

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