This paper proposes an improved strategy for allocating fire-fighting vehicles to Local Dispatch Centers (LDCs) over a region. By considering the uncertainties in fire location and intensity, and a flexible design, it develops a plan more cost-effective than that from a traditional deterministic approach, reducing the number and cost of required vehicles.
While small fires occur regularly and call for ‘first response’ equipment to be available close to susceptible fire-prone areas, large fires occur rarely and take time to develop. Thus the ‘extended attack’ equipment needed for larger fires can be held in reserve over a larger area, serving in effect as insurance against rare events. A layered strategy removes specialized equipment from the front lines and locates it in strategic reserves located in some of the pre-existing fire stations. The proposed solution thus reduces specialized equipment.
Innovatively, the analysis applies simulation to the management of fire-fighting resources, in order to deal with uncertainty in the number of deployments and location of fires over time. The approach first uses location algorithms to calculate optimal sets of fire stations. It then uses simulation to determine the probability distributions of outcomes of several performance measures (e.g., distance run by vehicles, vehicle utilization, or fire access time). The simulation implements flexibility by considering decision rules to open and close LDCs, according to recent observations of leading parameters. The outcomes of the simulation allow decision-makers to evaluate the performance of the flexible design in four different scenarios: as-is, intensification through wildland urban interface expansion or climate change, and attenuation (i.e., surveillance investment and law enforcement).
Finally, we use data available from ANPC and AFN/ICNF to compare the current design with the proposed flexible design, in a case study of the district of Porto, in Portugal. The results show that this design (a partially centralized strategy) leads to a cost-effective allocation of equipment and could reduce investment costs up to about 68 percent.