Evacuation planning is a crucial part of disaster management where the goal is to relocate people to safety and minimize casualties. Every evacuation plan has two essential components: routing and scheduling. However, joint optimization of these two components with objectives such as minimizing average evacuation time or evacuation completion time, is a computationally hard problem. To approach it, we present MIP-LNS, a scalable optimization method that utilizes heuristic search with mathematical optimization and can optimize a variety of objective functions. We also present the method MIP-LNS-SIM, where we further combine an agent-based model together with MIP-LNS to more accurately estimate the delay on roads due to congestion. We use real-world road network and population data from Harris County in Houston, Texas, and apply our methods to find evacuation routes and schedule for the area. We show that, within a given time limit, MIP-LNS finds better solutions than existing methods in terms of average evacuation time, evacuation completion time and optimality guarantee of the solutions. We also perform experiments with MIP-LNS-SIM to show its efficacy in estimating delays in the road network due to congestion by using an agent based model. Our results show that MIP-LNS-SIM can find efficient evacuation plans, and at the same time provide an estimate of the evacuation completion time for the given plan with a small percent error.

翻译：疏散规划是灾害管理中的关键环节，其目标是将人员转移至安全区域并最大限度减少伤亡。每个疏散计划包含两个核心要素：路线规划与时间调度。然而，以最小化平均疏散时间或疏散完成时间等为目标的这两个要素联合优化问题，在计算上具有高度复杂性。为解决这一问题，我们提出MIP-LNS方法——一种可扩展的优化方法，它通过启发式搜索结合数学优化，能够优化多种目标函数。此外，我们还提出MIP-LNS-SIM方法，该方法将智能体模型与MIP-LNS进一步结合，以更精确地估算道路拥堵造成的延误。我们利用得克萨斯州休斯顿哈里斯县的现实道路网络和人口数据，应用所提出的方法为该区域规划疏散路线与时间表。实验表明，在给定时间限制内，MIP-LNS在平均疏散时间、疏散完成时间及解决方案的最优性保证方面均优于现有方法。我们还通过MIP-LNS-SIM实验证明了其在基于智能体模型估算道路网络拥堵延误方面的有效性。结果表明，MIP-LNS-SIM能够找到高效的疏散计划，同时以较小百分比误差提供给定计划的疏散完成时间估算值。