Deploying Wildland Fire Suppression Resources with a Scenario-Based Standard Response Model

Robert G. Haight · Jeremy S. Fried

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Facility Location and Emergency Management Fire effects on ecosystems Military Defense Systems Analysis

Abstract

Wildland fire managers deploy suppression resources to bases and dispatch them to fires to maximize the percentage of fires that are successfully contained before unacceptable costs and losses occur. Deployment is made with budget constraints and uncertainty about the daily number, location, and intensity of fires, all of which affect initial-attack success. To address the deployment problem, we formulate a scenario-based standard response model with two objective functions: the number of suppression resources deployed and the expected daily number of fires that do not receive a standard response, defined as the desired number of resources that can reach the fire within a specified response time. To determine how deployment levels affect the standard response objective, a weighted sum of the objective functions is minimized, and the weights are ramped from large to small to generate the tradeoffs. We use the model to position up to 22 engines among 15 stations in the Amador-El Dorado unit of the California Department of Forestry and Fire Protection in central California. Each deployment is further evaluated in terms of expected number of escaped fires using CFES2, a stochastic simulation model of initial attack. The solutions of the standard response model form a tradeoff curve where increasing numbers of engines deployed reduces the expected daily number of fires not receiving the standard response. Solutions concentrate engines in a small set of centrally-located stations. We use a simple heuristic with CFES2 to incrementally remove engines based on simulation estimates of expected utilization frequency. The deployments obtained with the heuristic contain about the same number of fires as do solutions of the standard response model, but the heuristic solutions deploy engines to more stations.

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References

1974THE MAXIMAL COVERING LOCATION PROBLEM 1995Robust Optimization of Large-Scale Systems 1998Strategic facility location: A review 2006Facility location under uncertainty: a review 1983A Maximum Expected Covering Location Model: Formulation, Properties and Heuristic Solution 1989The Maximum Availability Location Problem 2006Simulation Optimization: A Review, New Developments, and Applications 1998The p-median problem in a changing network: the case of Barcelona

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