Automated Dynamic Resource Allocation for Wildfire Suppression

Between 2002 and 2012, the U.S. Forest Service and the Department of the Interior spent on average $3.13 billion per year on wildland fire protection [1]. Nearly $1 billion of that funding was devoted solely to fire suppression efforts. Currently, allocating resources for fire suppression is a subjective process, relying in many cases on individual commanders’ decisions that are based on insufficient information. An automated system would help commanders cope with situational uncertainty, time pressures, and limited resources by providing actionable recommendations [2]. Tactical wildland fire management is one example of problems involving resource allocation in highly uncertain, dynamic environments. Similar problems include urban search and rescue, flood monitoring and management, and earthquake response. Such problems involve high dimensionality (e.g., large number of locations or resources), uncertain dynamics, many combinations of resource assignments, and the balancing of multiple competing objectives. Dynamic resource allocation (DRA) problems are special cases of a more general class of problems called Markov decision processes (MDP) (see sidebar titled “Markov Decision Processes” on the following page). MDPs are a mathematical formulation of problems in which the system dynamics are partially random and partially controllable by a decision maker. MDPs are a reasonable model for wildfire suppression as well as for many other autonomous systems problems in defense. For example, MDPs can also be used to model fleet protection, military logistics, mine countermeasures, surveillance missions, battle management, comWildland fires pose a significant threat to life and infrastructure, and suppression efforts are costly in manpower and resources. Although important advances in situational awareness tools and communication technologies have greatly aided incident commanders in directing suppression efforts, there is still a lack of effective autonomous dynamic decision support systems that provide resource allocation recommendations to help commanders cope with the uncertainty of evolving fire dynamics and time pressures. » J. Daniel Griffith, Mykel J. Kochenderfer, Robert J. Moss, Velibor V. Mišić,