A mixed integer programming approach for asset protection during escaped wildfires

Incident management teams (IMTs) are responsible for managing the response to wildfires. One of the objectives of IMTs is the protection of assets and infrastructure. In this paper, we develop a mathematical model to assist IMTs in assigning resources to asset protection activities during wildfires. We present a mixed integer programming model for resource allocation with the aim of protecting the maximum possible total value of assets. The model allows for mixed vehicle types with interchangeable capabilities and with travel times determined by vehicle-specific speed and road network information. We define location-specific protection requirements in terms of vehicle capabilities. The formulated model extends classic variants of the team orienteering problem with time windows. The model capabilities are demonstrated using a hypothetical fire scenario impacting South Hobart, Tasmania, Australia. Computational testing shows that realistically sized problems can be solved within a reasonable time

Optimization Models and Algorithms for Vulnerability Analysis and Mitigation Planning of Pyro-Terrorism

In this dissertation, an important homeland security problem is studied. With the focus on wildfire and pyro-terrorism management. We begin the dissertation by studying the vulnerability of landscapes to pyro-terrorism. We develop a maximal covering based optimization model to investigate the impact of a pyro-terror attack on landscapes based on the ignition locations of fires. We use three test case landscapes for experimentation. We compare the impact of a pyro-terror wildfire with the impacts of naturally-caused wildfires with randomly located ignition points. Our results indicate that a pyro-terror attack, on average, has more than twice the impact on landscapes than wildfires with randomly located ignition points. In the next chapter, we develop a Stackelberg game model, a min-max network interdiction framework that identifies a fuel management schedule that, with limited budget, maximally mitigates the impact of a pyro-terror attack. We develop a decomposition algorithm called MinMaxDA to solve the model for three test case landscapes, located in Western U.S. Our results indicate that fuel management, even when conducted on a small scale (when 2% of a landscape is treated), can mitigate a pyro-terror attack by 14%, on average, comparing to doing nothing. For a fuel management plan with 5%, and 10% budget, it can reduce the damage by 27% and 43% on average. Finally, we extend our study to the problem of suppression response after a pyro-terror attack. We develop a max-min model to identify the vulnerability of initial attack resources when used to fight a pyro-terror attack. We use a test case landscape for experimentation and develop a decomposition algorithm called Bounded Decomposition Algorithm (BDA) to solve the problem since the model has bilevel max-min structure with binary variables in the lower level and therefore not solvable by conventional methods. Our results indicate that although pyro-terror attacks with one ignition point can be controlled with an initial attack, pyro-terror attacks with two and more ignition points may not be controlled by initial attack. Also, a faster response is more promising in controlling pyro-terror fires

THE NEXT GENERATION OF WILDLAND FIREFIGHTING TOOLS: USING UAV SWARMS FOR FIRE ATTACK

Wildland fires pose a direct threat to homeland security because of the severe personal, economic, and social stress they cause to those affected. As unmanned aerial vehicle (UAV) swarms become more ubiquitous in use, they will likely find a place as a frontline firefighting aerial asset, increasing the operational pace of aerial suppression flights and consequently increasing the safety of firefighters. This thesis explored the concept of using UAV swarms as a method for fire attack by comparing theoretical swarms to a conventional aerial asset within a realistic fire scenario and then using a systems engineering approach to define pressure points for implementing UAV swarms in the wildland space. The findings of this research support continued development of UAV swarms and clearly define areas that must be addressed before implementing large-scale UAV swarm flights. The firefighting UAV swarm system shows great promise due to its relative portability and ability to provide an aerial firefighting option to areas without ready access to conventional firefighting aircraft. It will be critical, however, to address logistical and communications constraints of UAV swarm systems before implementation to ensure positive outcomes.Civilian, Portland Fire and RescueApproved for public release. Distribution is unlimited

A review of operations research methods applicable to wildfire management

Across the globe, wildfire-related destruction appears to be worsening despite increased fire suppression expenditure. At the same time, wildfire management is becoming increasingly complicated owing to factors such as an expanding wildland-urban interface, interagency resource sharing and the recognition of the beneficial effects of fire on ecosystems. Operations research is the use of analytical techniques such as mathematical modelling to analyse interactions between people, resources and the environment to aid decision-making in complex systems. Fire managers operate in a highly challenging decision environment characterised by complexity, multiple conflicting objectives and uncertainty. We assert that some of these difficulties can be resolved with the use of operations research methods. We present a range of operations research methods and discuss their applicability to wildfire management with illustrative examples drawn from the wildfire and disaster operations research literature

Dynamic Resource Allocation in Disaster Response: Tradeoffs in Wildfire Suppression

Challenges associated with the allocation of limited resources to mitigate the impact of natural disasters inspire fundamentally new theoretical questions for dynamic decision making in coupled human and natural systems. Wildfires are one of several types of disaster phenomena, including oil spills and disease epidemics, where (1) the disaster evolves on the same timescale as the response effort, and (2) delays in response can lead to increased disaster severity and thus greater demand for resources. We introduce a minimal stochastic process to represent wildfire progression that nonetheless accurately captures the heavy tailed statistical distribution of fire sizes observed in nature. We then couple this model for fire spread to a series of response models that isolate fundamental tradeoffs both in the strength and timing of response and also in division of limited resources across multiple competing suppression efforts. Using this framework, we compute optimal strategies for decision making scenarios that arise in fire response policy

Initial attack fire suppression, spatial resource allocation, and fire prevention policy in California, the United States, and the Republic of Korea

In this dissertation, I combined a scenario-based, standard-response optimization model with a stochastic simulation model to improve the efficiency of the deployment of initial attack firefighting resources on wildland fires in California and the Republic of Korea. The optimization model minimizes the expected number of fires that do not receive a standard response—defined as the number of resources by type that must arrive at the fire within a specified time limit—subject to budget and station capacity constraints and uncertainty about the daily number and location of fires. The simulation model produces a set of fire scenarios in which a combination of fire count, fire locations, fire ignition times, and fire behavior occur. Compared with the current deployment, the deployment obtained with optimization shifts resources from the planning unit with the highest fire load to the planning unit with the highest standard response requirements. Resource deployments that result from relaxing constraints on station capacity achieve greater containment success by encouraging consolidation of resources into stations with high dispatch frequency, thus increasing the probability of resource availability on high fire count days. I extended the standard response framework to examine how a policy priority influences the optimal spatial allocation and performance of initial attack resources. I found that the policy goal of a fire manager changes the optimal spatial allocation of initial attack firefighting resources on a heterogeneous landscape, especially, for the socio-economic value of a potential fire location. Furthermore, I investigated the tradeoff between the number of firefighting resources and the level of fire ignition prevention efforts mitigating the probability of human-made fires in the Republic of Korea where most fires are caused by human activities. I found that fire ignition prevention is as cost-effective as initial attack resources given the current budget in the Republic of Korea on reducing the expected number of fires not receiving the standard response. From the comparison of the California and Republic of Korea cases, I can identify "rules of thumb" to be followed when allocating IA resources in particular ecological and policy settings