Hierarchical compromise optimization of ambulance locations under stochastic travel times

The location of ambulances is a crucial strategic decision for Emergency Medical Services (EMS). The base stations must achieve efficient dispatching under the inherent uncertainty of emergency locations and travel times. Additionally, managers need decision-support models that incorporate the multi...

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Autores: Gago Carro, Imanol, Aldasoro Marcellan, Unai, Lee, Dae-Jin, Merino Maestre, María
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/75015
Acceso en línea:http://hdl.handle.net/10810/75015
Access Level:acceso abierto
Palabra clave:stochastic programming
hierarchical compromise
branch-and-Fix coordination
OR in health services
CVaR
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spelling Hierarchical compromise optimization of ambulance locations under stochastic travel timesGago Carro, ImanolAldasoro Marcellan, UnaiLee, Dae-JinMerino Maestre, Maríastochastic programminghierarchical compromisebranch-and-Fix coordinationOR in health servicesCVaRThe location of ambulances is a crucial strategic decision for Emergency Medical Services (EMS). The base stations must achieve efficient dispatching under the inherent uncertainty of emergency locations and travel times. Additionally, managers need decision-support models that incorporate the multi-objective nature of such an efficient system. This paper bridges the gap between these requirements by developing a multi-objective hierarchical compromise optimization framework under stochastic travel times. Our hierarchical compromise optimization approach leverages quasi-optimal coverage solutions to provide EMS managers with flexibility in balancing (a) minimal average response time, (b) maximal resource adequacy, and (c) minimal worst-case response times. The stochasticity of travel times is incorporated into the models using a methodology to estimate continuous probability distributions for available and non-available historical data. The proposed modeling induces cross-scenario constraints, which are computationally challenging as the problem size increases. We tackle this issue by presenting an ad-hoc extension of a primal scenario-decomposition algorithm that deals with such constraints. This extension achieves superior performance over state-of-the-art optimization software. Finally, we use real-world data from the Basque Public Healthcare System to test the framework and prove the managerial interest of the obtained results.This research has been partially supported with the grant PID2 023-147410NB-100 and PID2023-153222OB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by ERDF/EU; with BCAM Severo Ochoa accreditation CEX2021-001142-S, Spain; and with the BERC 2022–2025 program, the project IT-1494-22 by the Basque Government, Spain. Imanol holds a PRE2020-091984 Severo Ochoa grant from the Spanish Ministry of Science and Innovation, Spain . Open Access funding is provided by the University of Basque Country.Elsevier202520252025info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/75015reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MICINN/PID2023-147410NB-100/info:eu-repo/grantAgreement/MICINN/PID2023-153222OB-I00/info:eu-repo/grantAgreement/MICINN/CEX2021-001142-S/https://www.sciencedirect.com/science/article/pii/S0305054825002369info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND licenseoai:addi.ehu.eus:10810/750152026-06-18T09:23:17Z
dc.title.none.fl_str_mv Hierarchical compromise optimization of ambulance locations under stochastic travel times
title Hierarchical compromise optimization of ambulance locations under stochastic travel times
spellingShingle Hierarchical compromise optimization of ambulance locations under stochastic travel times
Gago Carro, Imanol
stochastic programming
hierarchical compromise
branch-and-Fix coordination
OR in health services
CVaR
title_short Hierarchical compromise optimization of ambulance locations under stochastic travel times
title_full Hierarchical compromise optimization of ambulance locations under stochastic travel times
title_fullStr Hierarchical compromise optimization of ambulance locations under stochastic travel times
title_full_unstemmed Hierarchical compromise optimization of ambulance locations under stochastic travel times
title_sort Hierarchical compromise optimization of ambulance locations under stochastic travel times
dc.creator.none.fl_str_mv Gago Carro, Imanol
Aldasoro Marcellan, Unai
Lee, Dae-Jin
Merino Maestre, María
author Gago Carro, Imanol
author_facet Gago Carro, Imanol
Aldasoro Marcellan, Unai
Lee, Dae-Jin
Merino Maestre, María
author_role author
author2 Aldasoro Marcellan, Unai
Lee, Dae-Jin
Merino Maestre, María
author2_role author
author
author
dc.subject.none.fl_str_mv stochastic programming
hierarchical compromise
branch-and-Fix coordination
OR in health services
CVaR
topic stochastic programming
hierarchical compromise
branch-and-Fix coordination
OR in health services
CVaR
description The location of ambulances is a crucial strategic decision for Emergency Medical Services (EMS). The base stations must achieve efficient dispatching under the inherent uncertainty of emergency locations and travel times. Additionally, managers need decision-support models that incorporate the multi-objective nature of such an efficient system. This paper bridges the gap between these requirements by developing a multi-objective hierarchical compromise optimization framework under stochastic travel times. Our hierarchical compromise optimization approach leverages quasi-optimal coverage solutions to provide EMS managers with flexibility in balancing (a) minimal average response time, (b) maximal resource adequacy, and (c) minimal worst-case response times. The stochasticity of travel times is incorporated into the models using a methodology to estimate continuous probability distributions for available and non-available historical data. The proposed modeling induces cross-scenario constraints, which are computationally challenging as the problem size increases. We tackle this issue by presenting an ad-hoc extension of a primal scenario-decomposition algorithm that deals with such constraints. This extension achieves superior performance over state-of-the-art optimization software. Finally, we use real-world data from the Basque Public Healthcare System to test the framework and prove the managerial interest of the obtained results.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/75015
url http://hdl.handle.net/10810/75015
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICINN/PID2023-147410NB-100/
info:eu-repo/grantAgreement/MICINN/PID2023-153222OB-I00/
info:eu-repo/grantAgreement/MICINN/CEX2021-001142-S/
https://www.sciencedirect.com/science/article/pii/S0305054825002369
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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