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...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Basque Center for Applied Mathematics (BCAM) |
| Repositorio: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/2068 |
| Acceso en línea: | http://hdl.handle.net/20.500.11824/2068 |
| Access Level: | acceso abierto |
| Palabra clave: | Branch-and-Fix coordination CVaR Hierarchical compromise OR in health services Stochastic programming |
| Sumario: | 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. |
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