In this paper, a two-stage stochastic programming model is developed for the asset protection routing problem (APRP) to be employed in anticipation of an escaped wildfire. In this model, strategic and tactical decisions are considered in a two-stage setting. The locations of protection depots are determined, taking into account the routing decisions under different possible scenarios. To solve the proposed model, the Frank–Wolfe Progressive Hedging decomposition approach is employed. A realistic case study set in south Hobart, Tasmania, is considered. In this study, the scenarios for uncertain parameters are generated based on real data, considering different sources of uncertainties such as wind direction and speed and total monthly rainfall. Computational experiments have been conducted to demonstrate the solution algorithm’s efficiency in solving the asset protection routing problem with a two-stage stochastic framework. The numerical results suggest that more assets with higher values can be protected by considering the proposed two-stage stochastic programming model. The value of the approach is particularly significant where resources are limited, and uncertainty levels are high. Moreover, the model and solution procedure can be applied to other disaster situations in which protection activities occur.

在本文中，针对资产保护路由问题 (APRP) 开发了一个两阶段随机规划模型，以用于预测逃脱的野火。在这个模型中，战略和战术决策是在两阶段设置中考虑的。考虑到不同可能场景下的路由决策，确定保护站的位置。为了解决所提出的模型，采用了 Frank-Wolfe Progressive Hedging 分解方法。考虑了以塔斯马尼亚州南霍巴特为背景的现实案例研究。在这项研究中，不确定参数的情景是基于真实数据生成的，考虑了不同的不确定性来源，如风向和风速以及每月总降雨量。已经进行了计算实验，以证明求解算法在解决具有两阶段随机框架的资产保护路由问题中的效率。数值结果表明，通过考虑所提出的两阶段随机规划模型，可以保护更多具有更高价值的资产。在资源有限且不确定性水平高的情况下，该方法的价值尤其重要。此外，该模型和解决程序可以应用于发生保护活动的其他灾害情况。在资源有限且不确定性水平高的情况下，该方法的价值尤其重要。此外，该模型和解决程序可以应用于发生保护活动的其他灾害情况。在资源有限且不确定性水平高的情况下，该方法的价值尤其重要。此外，该模型和解决程序可以应用于发生保护活动的其他灾害情况。