In this study, we developed an optimization model and two iterative approaches to improve the efficiency of large fire management. This model allocates suppression effort across time and space to minimize fire loss within a defined duration. It departs from previous research by replacing
simplified fire containment rules with progress-based fire loss control. This is accomplished by extending the minimum travel time algorithm to build a large fire suppression model. Mixed integer programming is used to integrate spatial information such as fire behavior, firefighter safety,
and values at risk to guide large fire suppression. The concern of time-specific suppression allocation is modeled through two iterative approaches using different logical procedures. Test cases demonstrate how this model assembles spatial data to support suppression decisions for one fire
or multiple simultaneous fires. Suppression is also scheduled across 30 randomly simulated fires in Sequoia and Kings Canyon National Parks. Results demonstrate that suppression delay can significantly increase fire loss. Simulation results show that iterative approach A is more efficient
in distributing suppression effort into short time periods.
Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.