EURO 2024 Copenhagen
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1374. A Hierarchical Compromise Model and Matheuristic Algorithm for Stochastic Location-Allocation in Healthcare

Invited abstract in session TA-15: Location planning in healthcare, stream OR in Health Services (ORAHS).

Tuesday, 8:30-10:00
Room: 18 (building: 116)

Authors (first author is the speaker)

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

Abstract

In critical situations, the precision of emergency medical service (EMS) responses is of highest importance, as it can significantly impact patient outcomes. The effectiveness of EMS operations relies heavily on the strategic positioning and allocation of ambulances. This study addresses the ambulance location-allocation challenge in the Basque Country (Spain), where a fleet of 90 ambulances, including both Basic and Advanced Life Support resources, serves a population exceeding 2 million residents.

To tackle this issue, we leverage historical data and use a Box-Cox Cole and Green distribution to forecast response times. We propose a two-stage stochastic mixed 0-1 linear programming model aimed at optimizing the primary objective of maximizing expected coverage. Additionally, the model accounts for various secondary objectives, including minimizing average response time and incorporating risk aversion measures such as Conditional-Value-at-Risk, within a hierarchical compromise framework.

Due to the computational complexities inherent in this model, we introduce a novel matheuristic algorithm that combines primal decomposition and machine learning techniques. Computational experiments on medium and large-scale instances exhibits promising performance in efficiently addressing the intricate EMS optimization problem. This research contributes to enhancing the effectiveness and responsiveness of EMSs, leading to improved patient care during critical emergencies.

Keywords

Status: accepted

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