As part of the prototyping effort for the preparedness module (PM) of the Fire Program Analysis (FPA) system that IBM developed for five U.S. federal agencies, we designed and implemented an optimization model for determining budgets necessary for managing wildland fires during the initial response period. For a given budget, the model uses a mixed-integer linear optimization approach to maximize the number of acres managed (i.e., land protected from fire damage as a result of the initial response). The model is solved iteratively to establish a function that maps best achievable effectiveness, in terms of acres managed, at different budget levels. To handle the computationally prohibitive size of the resulting model instances, we devised a heuristic-based solution approach, and we reformulated the client's original model by switching to a continuous time domain and introducing piecewise-linearized functions. As a result, we not only built a tractable model, but also succeeded in delivering a performance speedup of more than 150 fold. We also conducted validation experiments for certain assumptions in the model to assess their impact on the solution quality.
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