Evacuation planning is a crucial part of disaster management. However, joint optimization of its two essential components, routing and scheduling, 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 combine agent-based simulation with MIP-LNS to estimate delays due to congestion, as well as, find optimized plans considering such delays. We use Harris County in Houston, Texas, as our study area. We show that, within a given time limit, MIP-LNS finds better solutions than existing methods in terms of three different metrics. However, when congestion dependent delay is considered, MIP-LNS-SIM outperforms MIP-LNS in multiple performance metrics. In addition, MIP-LNS-SIM has a significantly lower percent error in estimated evacuation completion time compared to MIP-LNS.

翻译：疏散规划是灾害管理的关键组成部分。然而，对其两个核心要素——路径规划与调度——进行联合优化（目标如最小化平均疏散时间或疏散完成时间）是一个计算难题。为应对这一挑战，我们提出了MIP-LNS，这是一种可扩展的优化方法，利用数学优化结合启发式搜索，能够优化多种目标函数。我们还提出了MIP-LNS-SIM方法，该方法将基于智能体的仿真与MIP-LNS相结合，用于估计拥塞导致的延迟，并在此基础上寻找考虑此类延迟的优化方案。我们以德克萨斯州休斯顿的哈里斯县为研究区域。结果表明，在给定的时间限制内，MIP-LNS在三种不同指标上均能比现有方法找到更优解。然而，在考虑拥塞依赖延迟时，MIP-LNS-SIM在多项性能指标上优于MIP-LNS。此外，与MIP-LNS相比，MIP-LNS-SIM在疏散完成时间的估计误差百分比显著更低。