Fire truck relocation during major incidents

The effectiveness of a fire department is largely determined by its ability to respond to incidents in a timely manner. To do so, fire departments typically have fire stations spread evenly across the region, and dispatch the closest truck(s) whenever a new incident occurs. However, large gaps in coverage may arise in the case of a major incident that requires many nearby fire trucks over a long period of time, substantially increasing response times for emergencies that occur subsequently. We propose a heuristic for relocating idle trucks during a major incident in order to retain good coverage. This is done by solving a mathematical program that takes into account the location of the available fire trucks and the historic spatial distribution of incidents. This heuristic allows the user to balance the coverage and the number of truck movements. Using extensive simulation experiments we test the heuristic for the operations of the Fire Department of Amsterdam‐Amstelland, and compare it against three other benchmark strategies in a simulation fitted using 10 years of historical data. We demonstrate substantial improvement over the current relocation policy, and show that not relocating during major incidents may lead to a significant decrease in performance

Severe weather-based fire department incident forecasting

For fire departments, having enough firefighters available during a shift is obviously an important requirement. Nevertheless, just like in any organization, having too many firefighters standby is not desirable from a financial point of view. Despite the fact that fire departments can and should not be run like production companies, at least for staffing purposes, forecasting the number of incidents that each fire station has to handle is highly relevant. In this paper, we develop models to create a forecast for the number of incidents that each fire station in the Dutch safety region Amsterdam-Amstelland has to handle for specific incident types and deal with major and small incidents. Previous studies mainly focused on multiplicative models containing correction factors for the weekday and time of year. Our main contribution is to incorporate the influence of different weather conditions in the categories of wind, temperature, rain, and visibility. Rain and wind typically have a strong linear influence, while temperature mainly has a non-linear influence. We show that an ensemble model has the best predictive performance

Increasing the responsiveness of firefighter services by relocating base stations in Amsterdam

In life-threatening situations where every second counts, the timely presence of firefighter services can make the difference between survival and death. Motivated by this, in collaboration with Fire Department Amsterdam-Amstelland in the Netherlands, we developed a mathematical programming model for determining the optimal locations of the vehicle base stations, and for optimally distributing firefighter vehicle types over the base stations. The model is driven by practical considerations. It (1) allows for fixing any subset of existing base locations that cannot be relocated (e.g., for historical reasons); (2) includes multiple vehicle types, each of which may have a type-dependent response-time target; and (3) includes crews that consist of arbitrary mixtures of professional (i.e., career) and volunteer firefighters. Extensive analysis of a large data set obtained from the Fire Department Amsterdam-Amstelland demonstrates: (1) that a reduction of over 50 percent in the fraction of firefighter late arrivals can be realized by relocating only three of the current 19 base locations; and (2) that adding new base locations to improve performance is unnecessary: optimization of the locations of the current base stations is as effective, and saves money. The results show an enormous potential for substantially reducing the fraction of late arrivals of firefighter services, with little investment in relocating a small number of stations