Emergency Logistics

In life-threatening situations where every second counts, the timely presence of emergency services (ambulance, firefighters, police) at the incident location can make the different between survival and death.

In life-threatening situations where every second counts, the timely presence of emergency services (ambulance, firefighters, police) at the incident location can make the different between survival and death. In order to reduce response times for emergency services, typical questions are: How can we accurately predict the occurrence of emergency incidents? What are the optimal base locations for emergency services? And how can we optimally (re)distribute the emergency vehicles in a service area?

The scientific challenge lies in the omnipresence of the phenomenon of uncertainty in many aspects of emergency service processes, including the volume and the locations of the emergency incidents, the travel times, the time required for on-the-scene emergency service, the availability of emergency vehicles, and external factors such as weather circumstances. To address these issues, we develop, analyze and evaluate stochastic optimization models and methods for smart and efficient planning of emergency services.
Our research leads to a better understanding of the stochastic behaviour of emergency-service processes, and to algorithms for demand prediction and for smart location and relocation of emergency resources (vehicles and personnel), both at the strategic and the operational level. In our research, we collaborate with many real-world parties, including ambulance service providers, the fire service, police and roadside assistance.

Contact person: Rob van der Mei
Research group: Stochastics (ST)
Research partners: GGD Flevoland, GGD Gooi- en Vechtstreek, Ambulance Amsterdam, RAV Utrecht, CityGIS, RIVM, Brandweer Amsterdam/Amstelland, National Politie, Stokhos Emergency Mathematics, CityGIS, ANWB, TU Delft
Spin-off: Stokhos