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

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

Operations research for decision support in wildfire management

The February 2009 ‘Black Saturday’ bushfires resulted in 173 fatalities, caused AUD$4 billion in damage and provided a stark reminder of the destructive potential of wildfire. Globally, wildfire-related destruction appears to be worsening with observed increases in fire occurrence and severity. Wildfire management is a difficult undertaking and involves a complex mix of interrelated components operating across varying temporal and spatial scales. This thesis explores how operations research methods may be employed to provide decision support to wildfire managers so as to reduce the harmful impacts of wildfires on people, communities and natural resources. Some defining challenges of wildfire management are identified, namely complexity, multiple conflicting objectives and uncertainty. A range of operations research methods that can resolve these difficulties are then presented together with illustrative examples from the wildfire and disaster operations research literature. Three mixed integer programming models are then proposed to address specific real-world wildfire management problems. The first model incorporates the complementray effects of fuel treatment and supression preparedness decisions within an integrated framework. The second model schedules fuel treatments across multiple time periods to maintain fire resistant landscape patterns while satisfying various ecological and operational requirements. The third model aggregates fuel treatment units to minimise total perimeter requiring management

A Spatial Optimization Model for Resource Allocation for Wildfire Suppression and Resident Evacuation

Wildland-urban interface wildfires have been a significant threat in many countries. This thesis presents an integer two-stage stochastic goal programming model for comprehensive, efficient response to wildfire including firefighting resource allocation and resident evacuation. In contrast to other natural disasters, the progression of wildfires depends on not only the probabilistic fire spread scenarios but also decisions made during firefighting. The proposed model optimizes the resource preparations before the fire starts and resource allocation decisions during the fire event. This model takes into account different wildfire spread scenarios and their impact on high-risk areas. The two objectives considered are minimizing the total cost of operations and property loss and minimizing the number of people at risk to be evacuated. A case study based on Santa Clara County in California, United States of America, is presented to demonstrate the model performance. Quantitative experiments show that this model can help to find efficient solutions by considering a trade-off between two objectives, and varying cell size based on scenarios reduces problem dimension and improves solution time

Emergency logistics for wildfire suppression based on forecasted disaster evolution

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces). © 2017 The Author(s

Empirical Analysis of Firefighting – Large-Fire Suppression in Victoria, Australia

Large fires can cause the deaths of hundreds of people, burn thousands of homes, and cost billions of dollars in damages. Suppression is the primary means of large-fire management. Most suppression costs, billions of dollars, come from large-fire suppression. Yet, formal knowledge of large-fire suppression is limited. Fire managers make decisions that impact the lives of thousands of people. As the effectiveness of large-fire suppression is mainly unquantified, there is little beyond their tacit knowledge to guide decisions. Before we address effectiveness, we must address a fundamental question: ‘How are suppression resources used on large fires?’ This thesis uses qualitative and quantitative methods to answer that question. This thesis examines the suppression of 74 large fires that occurred between 2010 and 2015 in Victoria, Australia. The Department of Environment, Land, Water and Planning made this research possible by providing operational data. The first step to resolving suppression resource use was to develop a framework of large-fire suppression (Chapter 3). A qualitative document analysis was performed on a subset of ten large fires. Three approaches were involved: 1) daily fire reconstructions were completed, covering 156 days, 2) a five-stage classification of suppression was developed by analysing the reconstructions and comments in 674 operational documents, and 3) content analysis was performed on the comments to classify discrete suppression tasks. Large-fire suppression was framed as a progression through five discrete stages with 20 identified tasks. A striking result was that 57% of resource use was on tasks that fall outside of current suppression modelling and productivity research