The problem of firebreak placement is crucial for fire prevention, and its effectiveness at landscape scale will depend on their ability to impede the progress of future wildfires. To provide an adequate response, it is therefore necessary to consider the stochastic nature of fires, which are highly unpredictable from ignition to extinction. Thus, the placement of firebreaks can be considered a stochastic optimization problem where: (1) the objective function is to minimize the expected cells burnt of the landscape; (2) the decision variables being the location of firebreaks; and (3) the random variable being the spatial propagation/behavior of fires. In this paper, we propose a solution approach for the problem from the perspective of simulation-based optimization (SbO), where the objective function is not available (a black-box function), but can be computed (and/or approximated) by wildfire simulations. For this purpose, Genetic Algorithm and GRASP are implemented. The final implementation yielded favorable results for the Genetic Algorithm, demonstrating strong performance in scenarios with medium to high operational capacity, as well as medium levels of stochasticity

翻译：防火带布局问题对于火灾预防至关重要，其景观尺度上的有效性取决于能否有效阻碍未来野火的蔓延。因此，为提供恰当应对，必须考虑火灾的随机性——从起火到熄灭均具有高度不可预测性。由此，防火带布局可视为一个随机优化问题：(1)目标函数为最小化景观预期烧毁面积；(2)决策变量为防火带的位置；(3)随机变量为火灾的空间传播/行为特征。本文提出一种基于仿真优化（SbO）的解决方案，其中目标函数不可显式获取（黑箱函数），但可通过野火仿真计算（和/或近似）。为此，我们实现了遗传算法与GRASP算法。最终实现表明，遗传算法在中高作业能力场景及中等随机性水平下均展现出优异性能。