A Distributionally Robust Optimization Framework for Ambulance Location and Dispatching under Travel Time Uncertainty

M. Merino Maestre, I. Gago Carro, U. Aldasoro Marcellan, A. Ruiz

Efficient Emergency Medical Services management involves complex decision-making problems. We propose a Distributionally Robust Optimization framework for the integrated ambulance location and dispatching problem, explicitly accounting for uncertainty in ambulance travel times, a key factor that is often neglected in the EMS optimization literature. The approach models ambiguity sets, providing protection against worst-case scenarios while avoiding the excessive conservatism of traditional robust optimization. Several variants are developed and evaluated through in-sample and out-of-sample experiments using historical data from the Basque Country. In addition, a Discrete Event Simulation model is used to assess the operational performance under realistic spatio-temporal demand patterns. The results show that DRO-based solutions offer a well-balanced trade-off between robustness and efficiency, highlighting their methodological and practical value for healthcare operations management.

Palabras clave: OR in health services, Stochastic programming, Robust optimization, Distributionally robust optimization, Location

GT SDDS 2: Emergencias y transporte sanitario
4 de septiembre de 2026  15:30
Aula 26

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